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This paper elaborates on the notion of language awareness among multilingual students. In the first part the author presents the results of a research study whose objective was to assess if multilingual students are aware that the languages they speak and study (English and German) interact constantly and if they recognize the phenomenon of the positive and negative transfer. The subjects were students of Applied Linguistics at the universities of Bydgoszcz, Gdańsk and Poznań in Poland. The second part of the article is an attempt to connect theory and practice. It contains some suggestions how to improve multilingual foreign language teaching. A comparison and discussion of grammar structures aims to promote language awareness among students of foreign languages.

This paper provides a developmental perspective on the concept of source text (ST) influence in translation. ST influence is regarded as any influence a ST may have on the production of a t...

It is common to have good declarative but poor procedural knowledge of a foreign language, especially for classroom learners. To study this gap in a constrained manner, we asked Chinese learners of English to repeat, correct and produce indirect speech. The indirect speech construction was selected in the present study because it is known to be a particularly complex construction. Chinese university students who all had good declarative knowledge of the rules governing indirect speech were selected to have overall low or high oral proficiency when assessed in a free speech situation. High proficiency participants pursued strategies that increased their speech rate while reducing errors. They used more idiomatic English, more chunked expressions, and showed less negative transfer from Mandarin, compared to low proficiency participants. Indeed use of chunks was the primary means by which both groups of participants were able to increase their accuracy, complexity, and speaking rate. Low proficiency but not high participants showed evidence of a speed-accuracy trade-off. They either kept errors low at the cost of high pausing, or produced many errors with the benefit of rapid speech. Identifying preferences for speed versus accuracy could facilitate methods for encouraging learners to move out of their comfort zones.

Šiuo tyrimu siekiama sužinoti, ar lietuviai, besimokantys anglų, kaip užsienio, kalbos daro klaidų dėl savo gimtosios kalbos įtakos. Straipsnyje tiriamas neigiamas perkėlimas iš lietuvių kalbos rašant angliškai, o pats tyrimas yra labiau kokybinis negu kiekybinis. Jame aptariamos klaidos, kurios iliustruojamos pavyzdžiais iš 34 forumų žinučių, kurias 2018 m. spalį parašė anglų kalbos B2 lygiu besimokantys pirmo arba antro kurso Vytauto Didžiojo universiteto studentai. Žinutėse jie reiškė savo nuomonę ir teikė grįžtamąjį ryšį apie praėjusią pristatymų savaitę anglų kalbos paskaitose: buvo rašoma, kas (ne)patiko kitų studentų pristatymuose, kas (ne)gerai pavyko, ką reikėtų patobulinti ateityje ir kita. Remiantis Camilleri (2004), dažniausiai pasitaikiusios neigiamo perkėlimo klaidos buvo suskirstytos į tipus; taip pat bandyta nustatyti tokių klaidų šaltinius (remiantis Camilleri, 2004; Brogan ir Son, 2015). Dauguma studentų rašydami angliškai darė klaidų ten, kur lietuvių kalboje tam tikra gramatinė kategorija nėra išskiriama, bet ji egzistuoja anglų kalboje, o kartais neigiamo perkėlimo klaidas įtakojo tiesioginis ar pažodinis vertimas iš lietuvių kalbos į anglų kalbą. Klaidų analizė parodė, kad anglų kalbos paskaitose didesnis dėmesys turėtų būti skiriamas veiksmažodžio formoms “is” ir “are”, “was” ir “were”, “has” ir “have”, artikeliams, kolokacijoms, laikų derinimui, kiekybiniams įvardžiams, anglų kalbos sakinių struktūrai ir angliškų žodžių daugiareikšmiškumui. This research intends to find out whether Lithuanians studying English as a foreign language make certain mistakes because of the influence of their native language. It focuses on negative transfer in writing in English and is qualitative rather than quantitative. The article discusses the errors and illustrates them with examples that come from a corpus for which the data was obtained from 34 Moodle forum posts written by English B2 students, native speakers of Lithuanian who were in year one or year two of their studies in various study programmes but also studied English at Vytautas Magnus University as an obligatory subject. The students participated in this forum in October 2018 and reflected on the week of presentations they had recently had: they were asked to write what they liked or did not like in the presentations their colleagues had given in class, what went well and what did not, what they should improve in the future, etc. This study identified the types of errors (based on Camilleri, 2004) that occurred most frequently and their source (based on Camilleri, 2004; Brogan & Son, 2015). Most frequently the students made errors in the cases where there was a specific grammatical category in English, but it was non-existent in Lithuanian, while sometimes the source of errors was literal translation from the native language. The error analysis shows that in the English classroom specific attention should be given to the verb forms “is” and “are”, “was” and “were”, “has” and “have”, articles, collocations, tense agreement, quantifiers, the sentence structure of the English language and the importance of word meanings.

This study examines the influence of previously acquired languages – namely, Colloquial Arabic (CA) and English – on the acquisition of Standard Arabic (SA) by L3 and L2 learners. It reports on the role of typological and structural proximity in language transfer and whether transfer patterns change over time. The study involved 105 participants: 41 CA-L1, English-L2 learners of SA, 47 English-L1 learners of SA, and 17 Arabic-L1 speakers. The participants completed three written tasks focusing on: definite article use, verb subcategorization rules, and sentential negation. The results indicate that CA plays a positive role in L3 learners’ acquisition of SA, mainly in forms where SA and CA converge, whereas English seems to play more of a negative role for both L3 and L2 learners. Thus, structural proximity seems to play a positive role in transfer to the L3. Negative transfer, irrespective of proximity/distance, diminishes as learners advance in their study of SA.

Članak se bavi kognitivno-lingvističkim pristupom frazemima (Broz 2015; Lakoff 1987) u talijanskome kao inome jeziku (Levorato i Cacciari 1992). Istražuje se motiviranost značenja i razumijevanje motiviranosti značenja frazema hrvatskih govornika talijanskoga kao inog jezika. Arbitrarnost jezičnoga znaka pri ovladavanju inim jezikom često dovodi do vrlo niske razine transparentnosti značenja novoučene riječi. U ovladavanju frazemima postoji olakotna okolnost da sličnost (strukturalna i značenjska) u hrvatskome i talijanskome jeziku olakšava njihovu interpretaciju (i razumijevanje njihova metaforičkoga značenja) te samo poznavanje značenja frazema hrvatskoga jezika automatski dovodi do visoke transparentnosti značenja talijanskih frazema (kao i obrnutoga ishoda: negativnoga prijenosa). Uzeli smo u obzir sve varijable koje mogu utjecati na interpretaciju frazema u J2 (Cardona 2008) među studentima inojezičnoga talijanskoga čiji je materinski jezik hrvatski. Preliminarnim ispitivanjem izdvojeni su najpoznatiji frazemi na hrvatskome jeziku (prema mišljenju ispitanika) te paralelno oni talijanski, a zatim su podijeljeni u četiri skupine: a) potpuni hrvatskotalijanski ekvivalenti, b) parcijalni (semantičko-formalni) i različiti talijanski ekvivalenti, c) nepoznati talijanski frazemi (s ekvivalentima i bez ekvivalenata) te d) zastarjeli hrvatski frazemi (kako bi se utvrdila paralela u interpretaciji i motivaciji tih frazema i onih iz treće skupine). Metodologija iz Levorato i Cacciari (1992) zapravo je prilagođena našemu istraživanju, gdje se osim poznatosti i transparentnosti značenja tražila i (ne)podudarnost s hrvatskim frazemima i na osnovi te selekcije odvojeno prezentirala ispitanicima., This article deals with the cognitive linguistic approach of idiomatic expressions in Italian language. It explores the motivation of meaning in phrases for Croatian L2 speakers of Italian language. The arbitrary character in one lexical unit (word) when learning a second language often leads to a very low level of meaning transparency of a newly learnt word. With idiomatic expressions there is a mitigating circumstance that similarity idioms in Croatian and Italian facilitate their interpretation (and their metaphorical meaning): understanding the meaning of Croatian phrases automatically leads to high transparency meaning of Italian idioms (and other way round: negative transfer). The survey was conducted on subjects with Croatian as their mother tongue, while their second language was Italian. The preliminary testing selected the most familiar idiomatic expressions in Croatian language, which are then specifically divided into four groups: a) absolute Croatian-Italian equivalents, b) partial equivalents (semantic-formal) and different equivalents, c) Italian idiomatic expressions without Croatian equivalents, and d) archaic Croatian idiomatic expressions (to determine parallels in interpretation and motivation with those in the third group). In the first group the hypotheses about motivation in meaning will be explained by the positive transfer of L1 to L2. In the second and third group we will try to interpret the results of motivation and transparency of meaning of half known and unknown idiomatic expressions using cognitive mechanisms of metaphor, metonymy and conventional knowledge, and by the level of knowledge of L2 or other foreign languages, as well as folk etymology. They will help us to explain what motivates higher or lower transparency of meaning in unfamiliar idioms of L2 (when there is no equivalent in L1, there is a partial equivalent, or there is an absolute equivalent). The aim of this study is to understand whether the same psycho-cognitive processes and mental images affect the interpretation of meaning in mother tongue and another foreign language.

The subject of the study is the phenomenon of pedagogical communication and interpersonal relations projected onto the teacher by students. The theoretical foundation for the study was formed by the ideas of psychoanalytic pedagogy, presented in the works of the modern Austrian psychoanalyst Helmuth Figdor. The main phenomenon investigated in the empirical part of this work is the phenomenon of transfer and its forms. Pedagogical transfer brings its negative contribution to the process of professional burnout, complicating the process of reflection on professional activity and provoking defensive counter-transference reactions aimed at students. The authors hypothesise that the transfer phenomenon is an element of pedagogical interaction and its existence may be empirically confirmed; based on prior experiences of communication, students associate a teacher with important figures from their past and project their qualities upon the teacher. In order to verify the hypothesis we used a colour-associative test developed by I. L. Solomin on the basis of the A. M. Etkind’s colour position test. The method of colour semantics is used to minimize distortions of social desirability typical to the tasks which require respondents to evaluate individuals involved in social roles tabooed for negative assessment. The sample consisted of 38 bachelor program graduates (correspondence and full-time) of the Institute of Psychology of the Herzen State Pedagogical University of Russia, St. Petersburg. The respondents were asked to associate each of the 48 concepts of the test with one of the 8 colours provided. The researchers than undertook an analysis of the colour-associative responses’ cluster structures. The preliminary results of the study suggest the presence of the transfer phenomenon in pedagogical communication in the forms of objective (fatherly), negative transfer, positively idealized, and supporting or analytical forms of transfer. Most often (in 57.9 % of cases) the teacher is endowed with the qualities of a controlling, evaluating and “punishing” figure, and at the same time the teacher is perceived to be indifferent and detached like a passer-by. That is, in the eyes of students, the teacher is primarily an influential figure, endowed with power and authority.

Many studies have shown that language acquisition has an important impact on Second Language Acquisition. The transfer of mother tongue falls into two kinds: positive transfer and negative transfer; positive transfer occurs when the habits of the two languages are similar; otherwise its negative transfer. Contrastive analysis, error analysis and inter-language theory have all deal with the negative transfer of language, which makes people understand native language’s negative transfer better and develops foreign language teaching. Chinese senior school students learn English on the basis of their Chinese. So they would be affected by their mother tongue and their knowledge. Based on that, the present writer wants to find out students’ problems in their English study by analyzing the mistakes according to language transfer theories. Such a study is also advantageous to the improvement of English teaching methods.

The transfer learning between the source and target domain has already achieved significant success in machine learning areas. However, the existing methods can not achieve satisfactory result when solving the two distant domains transfer learning problem. In the worst case, it could lead to the negative transfer. In this paper, we propose a novel framework called transitive transfer sparse coding (TTSC) to solve the two distant domains transfer learning problem. On the one hand, as an extension of the sparse coding, the TTSC framework constructs a robust and high-level dictionary across three different domains and simultaneously obtains three good feature sparse representations. On the other hand, TTSC utilizes the intermediate domain as a strong bridge to transfer valuable knowledge between the source domain and target domain. Empirical studies validated that the TTSC framework significantly could outperform state-of-the-art methods.

Unsupervised domain adaptation (UDA) aims to improve the classification performance on an unlabeled target domain by leveraging information from a fully labeled source domain. Recent approaches explore domain-invariant and class-discriminant representations to tackle this task. These methods, however, ignore the interaction between domain alignment learning and class discrimination learning. As a result, the missing or inadequate tradeoff between domain alignment and class discrimination are prone to the problem of negative transfer. In this paper, we propose Dynamic Weighted Learning (DWL) to avoid the discriminability vanishing problem caused by excessive alignment learning and domain misalignment problem caused by excessive discriminant learning. Technically, DWL dynamically weights the learning losses of alignment and discriminability by introducing the degree of alignment and discriminability. Besides, the problem of sample imbalance across domains is first considered in our work, and we solve the problem by weighing the samples to guarantee information balance across domains. Extensive experiments demonstrate that DWL has an excellent performance in several benchmark datasets., Comment: This paper has been accepted by CVPR2021

The breakthrough of social media has boosted to an increase in the spread of misleading information, with a serious impact on society especially when related to health knowledge. Recently, researchers have been developing AI-based automatic systems to detect rumours in social microblogs. Nevertheless rumours detection at the level of single post, also referred to as micro-level, is still a major challenge since most of the efforts have been directed toward the macro-level, which means that the system considers as rumours news carried by a set of aggregated microblog posts. In this work, we provide two contributions: first, we compare two state-of-the-art representations to figure out which one better catches hidden information in the data. Second, we explore whether it is possible to exploit knowledge extracted on a topic to automatically recognise micro-level rumour in a different one. To this end, we experimentally investigate three transfer learning methods on two health-related datasets. The comparison with a baseline that does not use any knowledge transfer from the source and target domains reveals that negative transfer occurs.

In order to explore the negative transfer of Guangzhou dialect on the British English pronunciation, this paper takes English major freshmen in Nanfang College of Sun Yat-sen University as respondents to participate into a phonetic test and analyzes their English pronunciation of vowels, consonants, stress, rhythm, and intonation, based on the data collected by a speech analyzer software, Praat and the evaluation done by foreign teachers (i.e. Ear Recognition Method). In conclusion, it is found that the respondents’ pronunciation of segmental phoneme and suprasegmental phoneme was negatively affected by Guangzhou dialect in different levels. In connection with the major findings, the authors provide four suggestions for English learners with Guangzhou dialect as mother tongue to improve their English pronunciation.

Nowadays, the studies on the language transfer are more concerning the impact of Chinese language on English learning. Very few of them are on the phenomenon of negative transfer of Tibetan language to the Tibetan students’ English learning under the trilingual(TIbetan, Chinese and English) environment. The author had a detailed analysis to the Tibetan students’ writing composition on the basis of language tranfer theories such as markedness, interlanguage hyposthesis, and etc. And figure out the main grammatical errors in their compositions through the method of Error Analysis. Contrastive Analysis is also used to have comparison among the Tibetan, Chinsed and English, and individual interview as well, the reasons for the grammatical errors are figured out. The major findings of this research are as follows: The grammatical errors emerged in the Tibetan English major students’ composition writing is in many fields, which includes omission, collocation, single and plural form of noun, subject - predicate concord, negative , possessive , relative clause and inverted sentence; Here is the reason for these grammatical errors: various thinking modes, in short of comparisons among the three languages and inadequate input. The methods can be applied to resolve are: language instructors should intsruct language learners propers through having comparison between the three languages and more importantly, strengthenging the students’ comprehensible and adequate input the learner langauge.

This article demonstrates how Cognitive Grammar and Construction Grammar can prevent Chinese students learning Spanish from fossilizing mistakes in restrictive relative clauses at the A2-B1 level of the European Framework of Reference for Languages. To address this issue, first, relative clauses in Spanish and Chinese were contrasted and, second, tailored solutions based on Cognitive Grammar were proposed. Among the cognitive based tailored solutions, certain geometry forms, colours and basic mathematics metaphors were compared with syntactic characteristics such as noun order, subordination hierarchy or resumption. To elucidate the impact of such teaching methods, an experiment with 74 Chinese students was performed. The results indicate that the efficacy of the proposed materials is statistically significant and as such, the Chinese students avoid fossilized mistakes while producing subject, object and locative relative clauses in Spanish.

In the field of second language acquisition, transfer is a common phenomenon. The mother language system has positive or negative effects on second language learning, namely positive transfer, and negative transfer. As the mother tongue of Chinese English learners, Chinese also has some interference in their English learning, especially in listening and speaking. Based on the experiences of teaching and learning English, this author analyzes the influence of negative transfer of Chinese on the cultivation of students' English listening and speaking abilities and discusses the corresponding countermeasures from the perspective of contrastive analysis between English and Chinese.

Cultural ecology has gradually become a trend that more people are familiar with it and attracted more people's attentions. However, in our foreign language teaching, compatriots’ excess of enthusiasm for English, educational subjects and objects’ contempt of native language culture, emphasis on the strength of input from target language culture and the negative transfer of mother language culture——these resulted in the great imbalance of English and Chinese Cultures. In order to avoid the embarrassing complexion of aphasia of Chinese culture in English-Chinese culture crash, this paper analyzes internal and external factors influencing the cultural ecology in foreign language teaching. Then it suggests adaptive instruction and the necessity of setting up the consciousness of China English, which benefit Chinese-English cultures’ equal dialogue and prevent the breakout of aphasia of Chinese culture in our foreign language teaching.

This study was conducted to identify, quantify, and compare the types and number of negative transfer errors in the written essays of HL and L2 learners of Russian, particularly to categorize all instances of negative transfer. It was also focused on determining which linguistic subsystem is most influenced by negative transfer from English to Russian. This study added a new dimension to the current linguistic knowledge concerning negative transfer and the literature available in the area of Russian language pedagogy. Sixty HL and 60 L2 learners of Russian of the intermediate mid level of proficiency enrolled in Russian language classes at UCLA participated in the study. Written essays submitted for the ACTR National Post-Secondary Russian Essay Contest on four topics were used as the primary data source.The most important finding from this research was that HL learners produced twice as many negative transfer errors as did L2 learners of Russian. Other important findings of the study reinforced the idea that writing in Russian showed the influence of English linguistic structures in four subsystems: lexical, semantic, syntactic, and orthographic. The research returned empirical evidence that, for both groups of learners, lexical and semantic subsystems were more vulnerable to the English-based linguistic structures than syntax and orthography.Conclusions from this study hold pedagogical implications for Russian language instruction. For example, comprehensible meaning-focused input at the vocabulary level should be a part of the curriculum and material design. Guidelines were offered for creating comprehensive instructional intervention and productive activities that can strengthen vocabulary development and assist learners in minimizing and overcoming negative language transfer.

In our daily lives, people encounter different problems in pattern recognition applications, such as fingerprint unlocking, faoe recognition, DNA detection, mother tongue culture transfer. The essenoe of pattern recognition is to match the known sample with the given pattern by comparing the known sample with the given pattern, so as to achieve the purpose of classification. In fuzzy sets, membership degree is often used to describe samples and patterns, and use fuzzy measure to calculate the similarity between samples and patterns, so that the overall characteristics of patterns can be well reflected, and samples are allowed to have a certain degree of interference. Sometimes, it is required to deduce the corresponding conclusions from the existing rule base through fuzzy reasoning algorithm, and sort them to get the classification results. Mother tongue transfer is an inevitable problem in English translation, which has a great influence on foreign language learning and translation. Language transfer usually includes four forms: overuse, avoidance, convenience and misuse, which can be divided into positive transfer and negative transfer. Due to the obvious differences between Chinese culture and English culture, negative transfer of mother tongue cannot be avoided, which increases the difficulty of English translation. This paper studies the pattern recognition based culture transfer of machine native language in the network environment.

In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political-related tweets is a challenging task due to the lack of explicit sentences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sentiment analysis to measure the non-clinical psychological distress status in Brexit tweets. The framework transfers the knowledge learnt from self-reported psychological distress tweets (source domain) to detect the distress status in Brexit tweets (target domain). The framework applies a domain adaptation technique to decrease the impact of negative transfer between source and target domains. The paper also introduces a Brexit distress index that can be used to detect levels of psychological distress of individuals in Brexit tweets. We design an experiment that includes data from both domains. The proposed model is able to detect the non-clinical psychological distress status in Brexit tweets with an accuracy of 66% and 62% on the source and target domains, respectively., Comment: SAC 2021, MLA - Machine Learning and its Applications

In the field of medical data mining, collecting sufficient expert labeled datasets especially with pixel-level annotations is extremely difficult. For diseases with scarce data, supervised learning has little effect even with synthetic images for data augmentation or cross-modality knowledge transfer. In response to the problem, we propose a novel solution - knowledge transfer across diseases. Existing transfer methods either focus on mapping one domain to another or focus on generating a subspace consisting of domain invariant features. However, these methods are negative in the transfer of knowledge across diseases because they lose the private characteristics of the target disease. Motivated by this limitation, we present a reweighting network (RW-net). It can be widely applied to a domain adaptation model based on adversarial, through reweighting dynamic balance subspace generation and segmenter optimization. We compare our method to two popular transfer learning methods and baseline on the open glioma dataset (BraTS) and ischemic stroke dataset (ATLAS). Our approach achieves excellent performance, even though the two diseases have completely different imaging traits.

L1 is, generally, believed to affect the acquisition of second language negatively. Because there is likely to be a negative transfer form L1 to L2, when the learner lacks sufficient knowledge for communicating his or her ideas in L2 and then draw upon the L1. The transfer includes both positive and negative transfer. However, the role of L1 is more often viewed as negative, causing negative transfer that results in a variety of errors. Generally, English and Chinese do not have many of the shared syntactical features. Hence the syntactic transfer is predictable when using the L2. This may lead to such errors as those in the noun phrase, in the verb phrase and various omissions. This paper analyses those errors in English writing, which represent the negative syntactic transfer from Chinese to English. Moreover, it discusses in details the particular causes for that transfer and propose improving the awareness of the syntactic distinction between these two languages in Chinese students.

Transfer learning seeks to improve the generalization performance of a target task by exploiting the knowledge learned from a related source task. Central questions include deciding what information one should transfer and when transfer can be beneficial. The latter question is related to the so-called negative transfer phenomenon, where the transferred source information actually reduces the generalization performance of the target task. This happens when the two tasks are sufficiently dissimilar. In this paper, we present a theoretical analysis of transfer learning by studying a pair of related perceptron learning tasks. Despite the simplicity of our model, it reproduces several key phenomena observed in practice. Specifically, our asymptotic analysis reveals a phase transition from negative transfer to positive transfer as the similarity of the two tasks moves past a well-defined threshold.

Głównym zagadnieniem poruszanym w monografii jest rozwój kompetencji gramatycznych związanych z użyciem i interpretacją form trybu conjuntivo przez studentów polskich uczących się języka portugalskiego jako obcego (L3). Przeprowadzone badanie koncentruje się na identyfikacji możliwych transferów negatywnych z 3 języków: polskiego (L1), angielskiego (L2) oraz hiszpańskiego (L2/L3), porównując ich częstotliwość u studentów o różnym poziomie zaawansowania w języku portugalskim (B1+ do C2 według ESOKJ), w kontekście wybranych zmiennych indywidualnych, np. wypracowanych strategii uczenia się czy zmian w motywacji. Wnioski z badania wykorzystane zostały do opracowania propozycji zmian metodycznych mających na celu zredukowanie ilości analizowanych transferów u polskich studentów o podobnej „historii” językowej. The main issue discussed in the monograph is the development of grammatical competence related to the use and interpretation of subjunctive forms by Polish students learning Portuguese as a foreign language (L3). The conducted study focuses on the identification of possible negative transfers from 3 languages: Polish (L1), English (L2) and Spanish (L2/ L3), comparing their frequency in students of various levels of advancement (B1 + to C2, according to CEFR), in the context of selected individual variables, e.g. developed learning strategies or changes in motivation. The conclusions from the study were used to develop some proposals for methodical changes aimed at reducing the number of analyzed transfers in Polish students with a similar linguistic "history". A questão principal discutida na monografia é o desenvolvimento da competência gramatical relacionada com o uso e interpretação das formas do conjuntivo por estudantes polacos que aprendem português como língua estrangeira (L3). O estudo realizado centra-se na identificação de possíveis transferências negativas de 3 línguas: polaco (L1), inglês (L2) e espanhol (L2 / L3), comparando a sua frequência em alunos de vários níveis (B1+ a C2, segundo QECR), no contexto de variáveis ​​individuais selecionadas, por exemplo, estratégias de aprendizagem ou mudanças na motivação. As conclusões do estudo foram utilizadas para desenvolver algumas propostas de mudanças metódicas destinadas a reduzir o número de transferências analisadas em estudantes polacos com uma "história" linguística semelhante.

As a whole linguistic unit, lexical chunks have the characteristics of prefabrication and extraction. Based on the relevant theories, this paper analyzes the existing problems in English writing teaching, expounds the advantages of lexical chunks in improving English writing ability, and puts forward some countermeasures to improve English writing ability .The problems include: the obsolete teaching mode of English writing, the influence of negative cultural transfer, the neglect of cultural infiltration in the process of writing, and the students' poor basic knowledge of English. The advantages include: reducing the negative transfer of mother tongue in writing, improving the fluency of English writing, quickly building the overall framework of the article, and improving the coherence and logic of the article. The countermeasures include: accumulating and enriching lexical chunks through various channels, paying attention to the cultural connotations contained in lexical chunks, training students' lexical chunks identification ability, strengthening the combination of lexical chunks and mind map, and improving students' autonomous learning ability of English lexical chunks.

As financial enterprises have moved their services to the internet, financial fraud detection has become an ever-growing problem causing severe economic losses for the financial industry. Recently, machine learning has gained significant attention to handle the financial fraud detection problem as a binary classification problem. While significant progress has been made, fraud detection is still a notable challenge due to two major reasons. First, fraudsters today are adaptive, inventive, and intelligent, making their fraud characteristics are too deep stealth to be detected by simple detection models. Second, labeled samples for training the detection models are usually very few as collecting large-scale training data needs a certain performance-time and is costly. To address the two problems, we propose a novel multi-source transfer learning approach with self-supervised domain distance learning for financial fraud detection problems. The core idea is to transfer relevant knowledge from multiple data-rich sources to the data-poor target task, e.g., learning fraud patterns from several other related mature loan products to improve the fraud detection in a cold-start loan product. Specifically, since the feature distribution discrepancy across domains may cause useless or even negative knowledge transfer, we propose self-supervised domain distance learning under the Wasserstein metric to measure the domain relevance/relationships between target and source tasks. The learned Wasserstein distance helps in selectively transferring most relevant knowledge from source domains to target domains. Thus it reduces the risk of negative transfer as well as maximizes the multi-source positive transfer. We conduct extensive experiments under multi-source few-shot learning settings on real financial fraud detection dataset. Experimental analysis shows that the inter-domain relationships learned by our domain distance learning model align well with the facts and the results demonstrate that our multi-source transfer learning approach achieves significant improvements over the state-of-the-art transfer learning approaches.

Based on data of 90 pieces of writings from two corpora WECCL and LOCNESS, the paper makes a study of the differentiation of texts by L1 and L2 learners in respect of lexical complexity. The tools used to process the data include AntConc, SPSS 19, the Web-based Lexical complexity analyzer - Single Mode etc. The findings of the research are as follows: first, there are significant underuses of modifiers and adverbs in L2 learners’ writings; second, there are more sophisticated words such as verbs, nouns, adjectives, adverbs, and modifiers in the L1 learners’ writings than in the L2 learners’ writings. Finally, some pedagogical suggestions are raised to enhance the L2 writing instruction. For instance, first, the L2 instructors should arouse the learners’ attention to the differences between the learners’ mother language and L2, to eradicate with efforts the negative transfer of the mother language of the L2 learner; second, the instructors should implant in the students more knowledge on English stylistics, by making clear difference between oral style and written style; third, the instructors should compile more updated English text books with authentic English material from online English corpora.

Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem. On the other hand, it is also recognized that if not executed properly, transfer learning algorithms could in fact impair the learning performance instead of improving it - commonly known as "negative transfer". In this paper, we study the online transfer learning problems where the source samples are given in an offline way while the target samples arrive sequentially. We define the expected regret of the online transfer learning problem and provide upper bounds on the regret using information-theoretic quantities. We also obtain exact expressions for the bounds when the sample size becomes large. Examples show that the derived bounds are accurate even for small sample sizes. Furthermore, the obtained bounds give valuable insight on the effect of prior knowledge for transfer learning in our formulation. In particular, we formally characterize the conditions under which negative transfer occurs., Comment: Paper accepted to ISIT2021

We present an embodied theory of transfer as applied to mathematical ideas. Project-based learning (PBL) offers important sites for investigating transfer because students and teachers must track mathematical invariant relations that are embedded in complex settings where knowledge and information is distributed and extended across various materials, symbolic inscriptions, and social interactions. We argue that transfer occurs when learners and teachers establish cohesion of their experiences by mapping modes of perceiving and acting that they successfully used in previous contexts to new contexts. Learners express that cohesion across contexts in a variety of ways, principally through speech, actions, and gestures, including gestural catchments and simulated actions. Students and teachers may engage in several forms of mapping, including explicit analogical mapping, constructing mappings implicitly via relational priming, and mapping relations via conceptual blending. Thus, we theorize that both teachers and students are integral to the transfer process. This embodied theory of transfer can account for near and far transfer, negative transfer, and “false” transfer, in which actors enact modes of perceiving and acting activated by cues that match only at a surface level. Embodied accounts of transfer have implications for educational practice. Some pedagogical approaches, such as concreteness fading, aim to foster and maintain cohesion of mathematical invariant relations. An embodied perspective on transfer can also inform the design of assessments, including both formative and summative assessments.

The cognitive ability to learn with experience is a hallmark of intelligent systems. The emerging transfer optimization paradigm pursues such human-like problem-solving prowess by leveraging useful information from various source tasks to enhance optimization efficiency on a related target task. The occurrence of harmful negative transfer is a key concern in this setting, paving the way for recent probabilistic model-based transfer evolutionary algorithms that curb this phenomenon. However, in applications where the source and target domains, i.e., the features of their respective search spaces (e.g., dimensionality) and the distribution of good solutions in those spaces, do not match, narrow focus on curbing negative effects can lead to the conservative cancellation of knowledge transfer. Taking this cue, this paper presents a novel perspective on domain adaptation in the context of evolutionary optimization, inducing positive transfers even in scenarios of source-target domain mismatch. Our first contribution is to establish a probabilistic formulation of domain adaptation, by which source and/or target tasks can be mapped to a common solution representation space in which their discrepancy is reduced. Secondly, a domain adaptive transfer evolutionary algorithm is proposed, supporting both offline construction and online data-driven learning of non-linear mapping functions. The performance of the algorithm is experimentally verified, demonstrating superior convergence rate in comparison to state-of-the-art baselines on synthetic benchmarks and a practical case study in multi-location inventory planning. Our results thus shed light on a new research direction for optimization algorithms that improve their efficacy by learning from heterogeneous experiential priors. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University This work was funded by the Agency for Science, Technology and Research (A*STAR) of Singapore, under the Singapore Institute of Manufacturing Technology-Nayang Technological University Collaborative Research Programme in Complex Systems. This work was also supported in part by the A*STAR Cyber-Physical Production System (CPPS) – Towards Contextual and Intelligent Response Research Program, under the RIE2020 IAF-PP Grant A19C1a0018. Yew-Soon Ong acknowledges the support by Singtel Cognitive and Artifcial Intelligence Lab for Enterprises (SCALE@ NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund ‐ Industry Collaboration Projects Grant.

Recently, evolutionary multi-tasking (EMT) has been proposed as a new search paradigm for optimizing multiple problems simultaneously. Since the beneficial knowledge can be transferred among tasks to speed up the optimization process, EMT shows better performance in many problems compared with single-task evolutionary search algorithms. Notably, existing works on EMT have been devoted to the evolutionary search with two tasks. However, when multiple tasks (i.e., number of tasks >2) are involved, the existing methods might fail as it is necessary to decide which is the most suitable task to be selected for performing the knowledge transfer. To address this issue, we propose an online credit information based task selection algorithm to enhance the performance of EMT. Specifically, a credit matrix is introduced to express the transfer qualities among tasks. Then, we design two kinds of credit information and propose an updating mechanism to adjust the credit matrix online. After that, a greedy task selection mechanism is proposed for balancing the exploration and exploitation of the task selection process. Besides, we also propose an adaptive transfer rate to enhance positive transfer while reduce the impact of negative transfer. In the experiment, we compare our method with the existing works. The results clearly demonstrate the efficacy of the proposed method.

Domain adaptation is proposed to generalize learning machines and address performance degradation of models that are trained from one specific source domain but applied to novel target domains. Existing domain adaptation methods focus on transferring holistic features whose discriminability is generally tailored to be source-specific and inferiorly generic to be transferable. As a result, standard domain adaptation on holistic features usually damages feature structures, especially local feature statistics, and deteriorates the learned discriminability. To alleviate this issue, we propose to transfer primitive local feature patterns, whose discriminability are shown to be inherently more sharable, and perform hierarchical feature adaptation. Concretely, we first learn a cluster of domain-shared local feature patterns and partition the feature space into cells. Local features are adaptively aggregated inside each cell to obtain cell features, which are further integrated into holistic features. To achieve fine-grained adaptations, we simultaneously perform alignment on local features, cell features and holistic features, within which process the local and cell features are aligned independently inside each cell to maintain the learned local structures and prevent negative transfer. Experimenting on typical one-to-one unsupervised domain adaptation for both image classification and action recognition tasks, partial domain adaptation, and domain-agnostic adaptation, we show that the proposed method achieves more reliable feature transfer by consistently outperforming state-of-the-art models and the learned domain-invariant features generalize well to novel domains. Nanyang Technological University This work was supported by Zhejiang Lab (2020KB0AC02), National Key R&D Program of China (SQ2020YFB130047, 2020YFB1313501, 2020YFB1313503), Zhejiang Provincial Natural Science Foundation (LR19F020005), the National Natural Science Foundation of China (61972347, 31070944, 31271147, 61922019, 31471063, 31671074, and 61572433) and the Fundamental Research Funds for the Central Universities, China (2017FZA7003). Qian Zheng is supported by the Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Singapore.

In the field of teaching Polish as a foreign language, transfer plays a major role. Positive transfer helps the users of closely related Slavic languages learn more quickly, while negative transfer should be closely monitored. Intercomprehension is a phenomenon which consists of guessing the meanings of related words and linguistic forms, and the ability to quickly understand languages which are closely related to the mother tongue of learners; in other words, it is a case of positive transfer. Intercomprehension in teaching related languages is directly associated with the phenomenon of language transfer. In the practice of teaching Polish as a non-native language in Slovakia and Czechia, teaching methods related to intercomprehension, including a contrast-based approach, have been applied for a long time. However, more focus has always been placed on negative transfer. In this article we provide examples of the impact of transfer, usually negative, at several linguistic planes in learning Polish by Slovaks and Slovak by Poles. The first author indicates two planes, those of inflection and syntax, using examples from the works of Polish students; the second author discusses the problems associated with lexis and indicates three planes: those of word formation, lexis, and style. Their discussions indicate that similarities help master a language more quickly and how important highlighting the differences for learners is. Interkomprehensja to zjawisko odgadywania znaczeń wyrazów i struktury języków pokrewnych, zdolność szybkiego rozumienia języków blisko spokrewnionych z językiem ojczystym uczących się. W glottodydaktyce nauczania języka polskiego w Słowacji i w Czechach interkomprehensję stosowano już dawno. Interkomprehensja w nauczaniu języków pokrewnych bezpośrednio jest związana ze zjawiskiem transferu językowego. W tym artykule podajemy przykłady na oddziaływanie transferu pozytywnego i negatywnego na różnych płaszczyznach językowych w przyswajaniu języka polskiego przez Słowaków i języka słowackiego przez Polaków. Potwierdza się, że podobieństwa pomagają szybciej opanować język i ważne jest uczulanie uczących się na różnice. Interkomprehensja w nauczaniu języków pokrewnych ma przede wszystkim wymiar językowy, ale może mieć również wymiar kulturowy.

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight widerange transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel selfsupervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easyto-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets., Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT 2020)

In recent years, many researchers have studied the mode of thinking and the negative transfer of mother tongue that affect students’ English writing, but little research has been done on independent college students. The author conducts the research on the students in Li Jiang College of GXNU which is an independent college. Based on the theories of Language Transfer, Contrastive Analysis, Error Analysis, the author attempts to study how negative transfer of Chinese influences EFL (English as a foreign language) learner in independent colleges on their English writing competence by analyzing the errors occurring in their English compositions with the help of correction function of Microsoft Word.

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.

Background One criterion of adaptive learning is appropriate generalization to new instances based on the original learning context and avoiding overgeneralization. Appropriate generalization requires understanding what features of a solution are applicable in a new context and whether the new context requires modifications or a new approach. In a series of three experiments, we investigate whether searching for an algebraic formalism before receiving direct instruction facilitates appropriate generalization. Results (1) Searching buffers against negative transfer: participants who first searched for an equation were less likely to overgeneralize compared to participants who completed a tell-and-practice activity. (2) Likelihood of creating a correct new adaptation varied by performance on the searching task. (3) Asking people to sketch alleviated some of the negative effects of tell-and-practice, but sketching did not augment the effect of searching. (4) When participants received more elaborate tell-and-practice instruction, the advantages of searching were less notable. Conclusions Searching for an algebraic formula prior to direct instruction may be a productive way to help learners connect a formula to its referent and avoid overgeneralization. Tell-and-practice instruction that only described the mathematical procedures led to the greatest levels of overgeneralization errors and worst performance. Tell-and-practice instruction that highlighted connections between the mathematical structure of the formula and the visual referent performed at similar or marginally worse levels than the search-first conditions.

Cross-dataset facial expression recognition (FER) has remained a challenging problem due to the obvious biases caused by diverse subjects and various collection conditions. To this end, domain adaption can be adopted as an effective solution by learning invariant representations across domains (datasets). However, FER requires special consideration of its specific problems e.g., uncertainties caused by ambiguous facial images, and diverse inter- and intra-class relationship. Such uncertainties already exist in single dataset FER, and could be significantly aggravated by enlarged class-wise discrepancies under cross-dataset scenarios. To mitigate this problem, this paper proposes an unsupervised domain adaptation method via regularized conditional alignment for FER, which adversarially reduces domain- and class-wise discrepancies while explicitly dealing with uncertainties within and across domain. Specifically, the proposed method effectively suppresses uncertainties in FER transfer tasks via: 1) semantics-preserving adaptation framework which enforces both domain-invariant learning and class-level semantic consistency between source and target expression data, where discriminative cluster structures are simultaneously retained; 2) auxiliary uncertainty regularization which further constrains the ambiguity of cluster boundaries to guarantee the transferring reliability, thus discouraging the negative transfer brought by divergent facial images. Evaluation experiments on publicly available datasets demonstrate that the proposed method significantly outperforms the current state-of-the-art methods.

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.

Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique.

Fine-tuning is an effective transfer learning method to achieve ideal performance on target task with limited training data. Some recent works regularize parameters of deep neural networks for better knowledge transfer. However, these methods enforce homogeneous penalties for all parameters, resulting in catastrophic forgetting or negative transfer. To address this problem, we propose a novel Inhomogeneous Regularization (IR) method that imposes a strong regularization on parameters of transferable convolutional filters to tackle catastrophic forgetting and alleviate the regularization on parameters of less transferable filters to tackle negative transfer. Moreover, we use the decaying averaged deviation of parameters from the start point (pre-trained parameters) to accurately measure the transferability of each filter. Evaluation on the three challenging benchmarks datasets has demonstrated the superiority of the proposed model against state-of-the-art methods.