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A political discourse would comprise terminology related to economic development, social welfare, national identity, international relations, and security. The purpose of this study was to determine the function of political discourse and study its role as a mass media that shapes public opinion, and to prove through discourse that political terminology plays a key role in shaping the country's image. Through a qualitative analysis of speeches, official documents, media coverage, and public statements, a dialectical approach was adopted to enhance understanding of the role of language in shaping perceptions of nations in the contemporary global context. The data mainly comprised secondary data, speeches of political leaders, official documents and media reports. Political archives, media reports and newspaper editorials also supplemented the data about Kazakhstan and its historical evolution. The research findings identified patterns, trends, and differences in the portrayal of a country's image and the strategies used to promote or defend it. It also found the nuanced interplay between political terminology, discourse, and the construction of a country's image. The findings would contribute to a deeper understanding of the role of language in shaping perceptions of nations in the contemporary global context.

Abstract Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.

The article examines one of the aspects of fuzzy emotivity — discursive emotivity. Fuzzy emotivity is an attempt to systematize those cases of communicative realization of emotivity that do not lend themselves to the three-part classification of emotives into affectives, connotatives and potentiatives. Discursivity is one of the obligatory characteristics of emotivity, since the evaluative sign and the semantic component of a word directly depends on the context, the communicative situation of actualization, horizontal and vertical "neighborhood" with other semiotic formations. Recently, the problems of urban or urban discourse have attracted the active attention of researchers, while the aspect of urban emotions or the emotions of the city has not become the subject of separate research. The choice of the research material (multimodal texts of advertising campaigns) made it possible to identify the key characteristics of the image of desired urban emotions, which contradicts the available sociolinguistic research on the emotional tonality of life in the city. The campaigns of suburban cottage settlements were also considered, in which the "secret" side of life in the city is objectified, problematic points of urban life are revealed. It is shown that urban emotions can be real and simulated when objective reality is replaced by emotive simulacra that affect the emotional sphere, i.e. have a manipulative effect on the recipient.

This study investigates the linguistic landscape of Kotabaru Malang Train Station through qualitative analysis of 41 signs, employing photographic documentation to capture linguistic features and functions. It reveals a prevalence of bilingual signage (Indonesian-English), emphasizing inclusivity and global connectivity. Sign functions include direction, information, advertising, disease prevention, prohibition, and warning, enhancing operational efficiency and passenger safety. Local identity is showcased through signage promoting Malang’s cultural heritage, fostering community pride and visitor engagement. Beyond station management, implications for urban planning and language policy highlight strategies for cultural sustainability and inclusive communication in public spaces. Investigating urban cultural not only enriches the understanding of societal diversity, but also informs inclusive urban development practices and effective public communication strategies. Future research could investigate how changes in government language policies and the integration of digital technologies impact the linguistic landscape and effectiveness of signage at transportation hubs.

Learning a language has become more accessible online butrelatively-few applied studies have explored the implications of technological advances, and the learners who use them (Chik and Ho 2017. "Learn a Language for Free: Recreational Learning among Adults." "System" 69: 162-171, 163). This mixed-methods study explores participation, exclusion and inclusion, in an Irish language MOOC, where participation reflects intrinsic learning (Deci and Ryan 1985. "Intrinsic Motivation and Self-Determination in Human Behavior." New York: Plenum), and cultural-oriented motivations (Van Duesen-Scholl 2003. "Towards a Definition of Heritage Language: Sociopolitical and Pedagogical Considerations." "Journal of Language, Identity and Education" 2 (3): 211-230). Using survey data (n = 1,523) and open-ended comments, learners' participation is conceptualised by exploring their demographic backgrounds. Tensions are demonstrated between the types of learning and motives of course participants, the metrics by which many online courses are viewed as successful, and the real value of learning a language for cultural, humanistic and intrinsic purposes (see Ushioda 2017. "The Impact of Global English on Motivation to Learn Other Languages: Towards an Ideal Multilingual Self." "The Modern Language Journal" 101 (3): 469-482). The case also explores issues of both exclusion, and inclusion, in distance language learning more generally.

Abstract Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems.

This paper examines early language learning (ELL) in Croatia, drawing context from broader European policies, recommendations, and research. It explores Croatia’s tradition, research, and practices in ELL within early childhood education and care institutions. The core analysis focuses on data from 2008 to 2023, examining foreign language programs in Croatian kindergartens (KGs). This includes the number of KGs offering programs, the languages available, and children’s participation. The research reveals a growing interest in early language exposure throughout Croatia. By analyzing trends over 14 years, the paper establishes how program offerings and participation rates have evolved. These findings offer valuable insights for educators, policymakers, and researchers in the field of ELL. Additionally, the research discusses the challenges and opportunities associated with implementing effective early language learning programs in Croatia.

This study draws on data collected from Chinese language textbooks to investigate the ways in which ethnic minority cultures and languages are represented in primary school textbooks in China. Drawing on the methods of thematic and comparative analysis, we specifically analyse how the representation of ethnic minorities in Chinese textbooks has changed across different periods in history. The analysis reveals that the representation of ethnic minority cultures and languages is largely limited to the "contributions approach" and "additive approach" to multicultural curriculum development. The findings show a largely selective, traditional, and stereotypical representation of ethnic minority cultures and languages in school textbooks, which focus primarily on cultures from representative ethnic groups. Although an accurate and equitable representation of ethnic minorities is still mostly lacking in Chinese language textbooks, a multicultural approach to ethnic diversity has been gaining ground over the past four decades. Interestingly, increasing interest in a multicultural approach has been accompanied by decreasing tolerance towards ethnic multilingualism in textbooks over the past four decades. Such observable trends towards monolingualism and multiculturalism in the representation of ethnic and linguistic diversity in textbooks echo the continuous changes in national education policy.

The article is devoted to identifying the features of the linguistic representation of the idea of revolutionary renewal of the world, which is the most important for the worldview system of E. Zamyatin. The study was carried out in line with the anthropological trend that is relevant in modern linguistics. The focus is on vocabulary related to the reflection of the concept of entropy - energy transferred by E. Zamyatin from the field of physical and mathematical knowledge to the field of the humanities. Much attention is paid to the analysis of the functional features of words combined into a lexical-semantic group with a common meaning of the subjects of the revolutionary process, as well as the representation of the concept of revolution in the writer’s journalism. The main objectives of the study are to determine the individual author’s component in the meanings of keywords and describe their systemic relationships. The main research method is contextual analysis. In the process of identifying actualized semes in the structure of lexical meaning, the method of component analysis is used; when determining the subtlest semantic nuances associated with the peculiarities of the writer’s individual worldview, the semantic-stylistic method is used. Historical and literary articles and essays by E. Zamyatin became the source of factual material. The result of the analysis was a description of a significant fragment of his individual picture of the world. The study of the writer’s journalism in the proposed perspective was undertaken for the first time. As a result, a conclusion was made about the principles of constructing a lexical microsystem that reflects the dialectical views of E. Zamyatin, the contextual meanings of keywords were identified, and methods for updating these meanings were described. The principle of binary oppositions, which underlies the writer’s comprehension of reality, determined the specifics of his idiostyle: the use of language game techniques based on the opposition of the meanings of a polysemic word, the active use of contextual antonyms and occasional words.

In recent years, transformer-based language representation models (LRMs) have achieved state-of-the-art results on difficult natural language understanding problems, such as question answering and text summarization. As these models are integrated into real-world applications, evaluating their ability to make rational decisions is an important research agenda, with practical ramifications. This article investigates LRMs’ rational decision-making ability through a carefully designed set of decision-making benchmarks and experiments. Inspired by classic work in cognitive science, we model the decision-making problem as a bet. We then investigate an LRM’s ability to choose outcomes that have optimal, or at minimum, positive expected gain. Through a robust body of experiments on four established LRMs, we show that a model is able to ‘think in bets’ if it is first fine-tuned on bet questions with an identical structure. Modifying the bet question’s structure, while still retaining its fundamental characteristics, decreases an LRM’s performance by more than 25%, on average, although absolute performance remains well above random. LRMs are also found to be more rational when selecting outcomes with non-negative expected gain, rather than optimal or strictly positive expected gain. Our results suggest that LRMs could potentially be applied to tasks that rely on cognitive decision-making skills, but that more research is necessary before these models can robustly make rational decisions.

Language representation learning is a vital field in Natural Language Processing (NLP) that aims to capture the intricate semantics and contextual information of text. With the advent of deep learning and neural network architectures, representation learning has revolutionized the NLP landscape. However, the majority of research in this field has concentrated on resource-rich languages, putting Low-Resource Languages (LRL) at a disadvantage due to their limited linguistic resources and the absence of pre-trained models. This paper addresses the significance of language representation learning in a low-resource language, Greek, and its impact on various downstream tasks that heavily rely on semantically and contextually enriched language representations. Accurate classification requires an understanding of nuanced linguistic cues and contextual dependencies. Effective representations bridge the gap between raw text data and classification models, encoding semantic meaning, syntactic structures, and contextual information. By leveraging various representation learning techniques using Transformer-based Language Models (LM), such as domain-adaption and contrastive learning, we aim to enhance the performance of text classification in this LRL setting. We explore the challenges and opportunities in developing effective representations and propose a multi-stage LM pre-training and meta-learning approach to improve performance in classification downstream tasks. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, press text clippings and internet articles such as blog posts and opinion pieces. The results show significant improvements in the classification effectiveness of each task in terms of micro-averaged F1-score in sentiment, irony, hate speech, emotion and three custom classification tasks.

Despite the continuously expanding presence of Spanish-speaking communities in the United States, media representation of the Spanish language and that of its speakers has remained relatively scarce. At present, however, a growing interest in reaching and cashing in on this influential consumer group is forcing significant changes in the mass media communication landscape. Not only are an increasing number of movies and TV shows working with more diverse casts (ethnically, culturally, linguistically, etc.), but there also seems to be a heightened presence of Latinx characters in leading or supporting roles. This tendency, however, does not necessarily mean that mainstream media is becoming more inclusive and less anglocentric. In fact, a careful look at the storylines of individuals who are perceived to be Spanish speakers will reveal that they mostly portray stereotypical roles and behaviors. When their stories are told, they are all too often infused with unwarranted messages that portray Latinxs as lazy, unskilled, unintelligible in speech, hypersexual, or simply too ‘foreign’ to fit in. Whether overtly or covertly expressed, the negative impact of these persistent transgressions has the potential to shape real-world ideologies, attitudes, and prejudices. This paper adopts a critical raciolinguistic perspective, which underscores the co-naturalization of language and race, to highlight the role of media in reinforcing discrimination against the Spanish language and its speakers. In our analysis of six recently popularized TV shows (i.e., East Los High, Family Guy, Gentefied, Jane the Virgin, One Day at a Time, and That ‘70s Show), we examine the perpetuation of racialized stereotypes toward Latinx characters’ linguistic practices with regard to (i) the environment in which they exist, (ii) their mannerisms, (iii) speech patterns, and (iv) interactions with other characters. We demonstrate how these shows’ blending of seemingly harmless linguistic ideologies with stereotypical and sensationalized representations grounded in colonial hierarchies reproduces hegemonic interests, perpetuates social inequalities, and places racialized Spanish speakers at a disadvantage.

Family dogs are exposed to a continuous flow of human speech throughout their lives. However, the extent of their abilities in speech perception is unknown. Here, we used functional magnetic resonance imaging (fMRI) to test speech detection and language representation in the dog brain. Dogs (n = 18) listened to natural speech and scrambled speech in a familiar and an unfamiliar language. Speech scrambling distorts auditory regularities specific to speech and to a given language, but keeps spectral voice cues intact. We hypothesized that if dogs can extract auditory regularities of speech, and of a familiar language, then there will be distinct patterns of brain activity for natural speech vs. scrambled speech, and also for familiar vs. unfamiliar language. Using multivoxel pattern analysis (MVPA) we found that bilateral auditory cortical regions represented natural speech and scrambled speech differently; with a better classifier performance in longer-headed dogs in a right auditory region. This neural capacity for speech detection was not based on preferential processing for speech but rather on sensitivity to sound naturalness. Furthermore, in case of natural speech, distinct activity patterns were found for the two languages in the secondary auditory cortex and in the precruciate gyrus; with a greater difference in responses to the familiar and unfamiliar languages in older dogs, indicating a role for the amount of language exposure. No regions represented differently the scrambled versions of the two languages, suggesting that the activity difference between languages in natural speech reflected sensitivity to language-specific regularities rather than to spectral voice cues. These findings suggest that separate cortical regions support speech naturalness detection and language representation in the dog brain.

The article is devoted to the analysis of the conceptual component of the name of the concept “Nachhaltigkeit” on the basis of lexicographic sources. This concept is becoming increasingly popular in the German linguistic culture. Its popularity is due to the change in the socio-economic para- digm in Germany and other countries of the world. Concept names (Nachhaltigkeit/nachhaltig) are often found in various discourses (political, economic, advertising, etc.). The study within the framework of this paper included the analysis of the concept name’s definitions, its synonyms, compound lexemes with the component -nachhaltig and the identification of conceptual features. The analysis was based on the most popular German lexicographic sources. The article also provides an analysis of the approximate analogues of the name of the concept in English (sustainable / sustainability) and Russian (ustoychivoye razvitiye) from the point of view of the conceptual component. Similarities and differences are revealed. The article lists the conceptual features of the concept, gives a generalized definition of the name of the concept in the German linguistic culture.

AbstractIt is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep‐learning‐based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high‐quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine‐tuned a pre‐trained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence‐transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end‐to‐end learning by disallowing any use of hand‐crafted or dependency‐parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.

Although Arabic is the native language of a fifth of the Israeli population, its representation in the Israeli linguistic landscape (LL) is problematic. Adopting a sociolinguistic framework that underscores the political nature of language (Ben-Rafael 2009), this study invited Palestinian-Israeli college students to reflect on the linguistic reality in their area through the participatory documentary photography tool ‘PhotoVoice’. The students captured photos of linguistic landscapes, analysed them, and wrote commentaries voicing their reflections. Students ‘PhotoVoices’ shed light on the abundance of mistakes in Arabic words on the signs. Content analysis of the ‘PhotoVoices’ on this issue highlighted students’ cognitive and emotional perceptions and emerging awareness of the explicit and implicit messages delivered through the signage. [ABSTRACT FROM AUTHOR]

In recent years, neural networks have been widely used in natural language processing, especially in sentence similarity modeling. Most of the previous studies focused on the current sentence, ignoring the commonsense knowledge related to the current sentence in the task of sentence similarity modeling. Commonsense knowledge can be remarkably useful for understanding the semantics of sentences. CK-Encoder, which can effectively acquire commonsense knowledge to improve the performance of sentence similarity modeling, is proposed in this paper. Specifically, the model first generates a commonsense knowledge graph of the input sentence and calculates this graph by using the graph convolution network. In addition, CKER, a framework combining CK-Encoder and sentence encoder, is introduced. Experiments on two sentence similarity tasks have demonstrated that CK-Encoder can effectively acquire commonsense knowledge to improve the capability of a model to understand sentences.

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.

The article describes the originality of the writer’s picture of the world and the peculiarities of the representation of the value system in а literary text. The purpose of the study is to reconstruct the system of value orientations presented in a work of art. The subject of the study is the system of values and axiological guidelines of the writer, indirectly or directly represented in the text. The object of the study is the axiological system of Nina Gorlanova, presented in the collection of stories Svetlaya proza. Research material – stories from Svetlaya proza by Nina Gorlanova. Research methods are determined by the research objectives and include general scientific methods of analysis and synthesis, a combination of linguistic (descriptive, statistical, semantic methods) and literary (problem-thematic method) methods; an axiological approach is used to analyze a literary text. The novelty of the study is due to its interdisciplinary nature. In this work, an attempt is made to integrate the knowledge of linguistics, literary criticism and axiology. Research results. This paper makes an attempt to demonstrate the axiological guidelines of the writer and their hierarchical structure, which made it possible to include value dominants in the interpretation of the writer’s artistic world. Practical implications. The results of this work can be used in the study of disciplines such as text stylistics, poetics of a work of art, as well as in the analysis of fiction speech.

Sentiment analysis of online reviews is an important task in natural language processing. It has received much attention not only in academia but also in industry. Data have become an important source of competitive intelligence. Various pretraining models such as BERT and ERNIE have made great achievements in the task of natural language processing, but lack domain-specific knowledge. Knowledge graphs can enhance language representation. Furthermore, knowledge graphs have high entity / concept coverage and strong semantic expression ability. We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT. To improve the interpretability of the deep learning algorithm, we construct an SAKG in which triples are injected into sentences as domain knowledge. Our investigation reveals promising results in sentence completion and sentiment analysis tasks.

Introduction. The article discusses the linguistic manifestation of prohibition semantics in Tuvan, analyzes the grammatical and lexical means of its expression, determines the semantic differences of the prohibition indicators and their functional/stylistic features. Goals. The paper aims to describe the grammatical and lexical means of expressing the semantics of prohibition in modern Tuvan, to analyze their meanings, pragmatic and stylistic functions. Materials and Methods. The study employs the descriptive method, functional/semantic and communicative/pragmatic approaches. The research material was provided by examples from digital sources of Tuvan-language texts available at: http://www.tuvancorpus.ru/. Results. The research of Tuvan language materials confirms the data from typologically different languages that indicators of the negative imperative are stylistically universal transmitters of the prohibitive meaning. Other means of expressing prohibition — besides the negative form of the future tense –бас — are stylistically marked. When it comes to express a prohibition, intonation holds an important place since it interacts with the former’s formal indicators proper, context and communicative situation. The paper identifies several types of prohibition (the prohibitive) that have received grammatical and lexical expressions in the Tuvan language — the regulative, preventive, constative, and corrective. Semantically, a comparatively wider range of prohibition values is possessed by the analytical form Tv-п болбас ― a special grammatical indicator of prohibition expressing the regulative, preventive, and constative. For other grammatical means within this system of indicative indicators, transfer of prohibition values is not a key function of theirs. This is the case of the future negative -бас (regulative, preventive) and the past -ды. The latter conveys a value of the admonitive. The system of lexical and grammatical means of expressing prohibition is distinguished by the lexemes хоржок (preventive), болзун, адыр (corrective) that have no certain part of speech status within the modern language. The prohibition lexeme болзун is a grammaticalization of the verb бол- in the meaning ‘to finish’ for the third imperative. There are two verbs бол- and сокса- that implicitly express prohibition/correction. The considered lexicogrammatical and lexical means of prohibition belong to the colloquial style. The formal style employs a number of special lexemes with the meaning of the regulative formed from the stem хору-. This word-formation nest includes verbs, adjectives, adverbs. The results of the study in the functional/ semantic field of prohibition in the Tuvan language shall be demanded in further research on modality in the Turkic and other languages

AbstractPre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M1 , a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER.

The intermediate language adopted in existing Web vulnerability detection methods is designed for a specific programming language. However, when abstracting a vulnerability from the source code of multiple programming languages, a unified intermediate language cannot represent the same type of vulnerability in multiple languages, which increases the difficulty of subsequent vulnerability analysis. To deal with this problem, this paper proposed an intermediate language based on taint analysis to realize the unified abstract representation of the same type of vulnerability information in multiple programming languages. In the design process of the intermediate language, it firstly abstracted the process of vulnerability as a triple representation, and the code elements related to the triple were abstracted as the keywords of the intermediate language. Secondly, it designed the syntax of the intermediate language according to the semantic relationship between the triples. Thirdly, it utilized the taint analysis method to trace the execution path of the taint source when escaping, and the intermediate language representation could be obtained by escaping the source code in the path. Finally, it applied the intermediate language to the vulnerability detection model. The experimental results show that compared with the other intermediate languages' abstract representation of vulnerability information in programming languages, the proposed intermediate language is more universal and more effective for vulnerability detection. [ABSTRACT FROM AUTHOR]

In the last 5 years, language representation models, such as BERT and GPT‐3, based on transformer neural networks, have led to enormous progress in natural language processing (NLP). One such NLP task is commonsense reasoning, where performance is usually evaluated through multiple‐choice question answering benchmarks. Till date, many such benchmarks have been proposed, and 'leaderboards' tracking state‐of‐the‐art performance on those benchmarks suggest that transformer‐based models are approaching human‐like performance. Because these are commonsense benchmarks, however, such a model should be expected to generalize, that is, at least in aggregate, should not exhibit excessive performance loss across independent commonsense benchmarks regardless of the specific benchmark on (the training set of) which it has been fine‐tuned. In this article, we evaluate this expectation by proposing a methodology and experimental study to measure the generalization ability of language representation models using a rigorous and intuitive metric. Using five established commonsense reasoning benchmarks, our experimental study shows that the models do not generalize well, and may be (potentially) susceptible to issues such as dataset bias. The results therefore suggest that current performance on benchmarks may be an over‐estimate, especially if we want to use such models on novel commonsense problems for which a 'training' dataset may not be available, for the language representation model, to fine‐tune on. [ABSTRACT FROM AUTHOR]

The Shawl by Cynthia Ozick is an extremely intriguing attempt at writing hunger, loss and the Holocaust. The novella transforms into a meditation on the possibility of depicting traumatic sensations, which easily defy symbolisation. As she casts suspicion on language and literature, and more broadly representation, Ozick adheres to the tradition of Jewish aniconism. However, she does so in an ostentatiously literary manner, verging on the idolatry of fiction. Ozick discards verisimilitude and hyperrealism in the representations of hunger and the Holocaust. In so doing, she suggests that the illusion of immediacy they produce is merely a fetish rather than the literary celebration of the body. Ozick’s ambitions may be more moderate, but they are certainly more honest. She explores the irreconcilable differences between the realms of the sensual and the literary. However, she also seems to say “literature in spite of all” (to misquote Georges Didi-Huberman’s dictum), thereby articulating her affirmation of the linguistic medium and literature despite all their shortcomings and deficiencies.

It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep‐learning‐based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high‐quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine‐tuned a pre‐trained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence‐transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end‐to‐end learning by disallowing any use of hand‐crafted or dependency‐parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR. [ABSTRACT FROM AUTHOR]

Mapping brain functions is crucial for neurosurgical planning in patients with drug-resistant seizures. However, presurgical language mapping using either functional or structural networks can be challenging, especially in children. In fact, most of the evidence on this topic derives from cross-sectional or retrospective studies in adults submitted to anterior temporal lobectomy. In this prospective study, we used fMRI and DTI to explore patterns of language representation, their predictors and impact on cognitive performances in 29 children and young adults (mean age at surgery: 14.6 ± 4.5 years) with focal lesional epilepsy. In 20 of them, we also assessed the influence of epilepsy surgery on language lateralization. All patients were consecutively enrolled at a single epilepsy surgery center between 2009 and 2015 and assessed with preoperative structural and functional 3T brain MRI during three language tasks: Word Generation (WG), Rhyme Generation (RG) and a comprehension task. We also acquired DTI data on arcuate fasciculus in 24 patients. We first assessed patterns of language representation (relationship of activations with the epileptogenic lesion and Laterality Index (LI)) and then hypothesized a causal model to test whether selected clinical variables would influence the patterns of language representation and the ensuing impact of the latter on cognitive performances. Twenty out of 29 patients also underwent postoperative language fMRI. We analyzed possible changes of fMRI and DTI LIs and their clinical predictors. Preoperatively, we found atypical language lateralization in four patients during WG task, in one patient during RG task and in seven patients during the comprehension task. Diffuse interictal EEG abnormalities predicted a more atypical language representation on fMRI (p = 0.012), which in turn correlated with lower attention (p = 0.036) and IQ/GDQ scores (p = 0.014). Postoperative language reorganization implied shifting towards atypical language representation. Abnormal postoperative EEG (p = 0.003) and surgical failures (p = 0.015) were associated with more atypical language lateralization, in turn correlating with worsened fluency. Neither preoperative asymmetry nor postoperative DTI LI changes in the arcuate fasciculus were observed. Focal lesional epilepsy associated with diffuse EEG abnormalities may favor atypical language lateralization and worse cognitive performances, which are potentially reversible after successful surgery.