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Abstract The rise of social media has led to an increase in hate speech. Hate speech is generally described as a deliberate act of aggression aimed at a particular group, intended to harm or marginalize them based on specific attributes of their identity. While positive interactions in diverse communities can greatly enhance confidence, it is important to acknowledge that negative remarks such as hate speech can weaken community unity and present a significant impact on people’s well-being. This highlights the need for improved monitoring and guidelines on social media platforms to protect individuals from discriminatory and harmful actions. Despite extensive research on resource-rich languages, such as English and German, the detection and analysis of hate speech in less-resourced languages, such as Norwegian, remains underexplored. Addressing this gap, our study leverages a metalinguistic approach that uses advanced meta-learning techniques to enhance the detection capabilities across bilingual texts, effectively linking technical advancements directly to the pressing social issue of hate speech. In this study, we introduce techniques that adapt models that deal with hate speech detection within the same languages (intra-lingual), across different languages (cross-lingual), and techniques that adapt models to new languages with minimal extra training, independent of the model type (cross-lingual model-agnostic meta-learning-based approaches) for bilingual text analysis in Norwegian and English. Our methodology incorporates attention mechanisms (components that help the model focus on relevant parts of the text) and adaptive learning rate schedulers (tools that adjust the learning speed based on performance). Our methodology incorporates components that help the model focus on relevant parts of the text (attention mechanisms) and tools that adjust the learning speed based on performance (adaptive learning rate schedulers). We conducted various experiments using language-specific and multilingual transformers. Among these, the combination of Nor-BERT and LSTM with zero-shot and few-shot model-agnostic meta-learning achieved remarkable F1 scores of 79% and 90%, highlighting the effectiveness of our proposed framework.

The rise of social media has led to an increase in hate speech. Hate speech is generally described as a deliberate act of aggression aimed at a particular group, intended to harm or marginalize them based on specific attributes of their identity. While positive interactions in diverse communities can greatly enhance confidence, it is important to acknowledge that negative remarks such as hate speech can weaken community unity and present a significant impact on people's well-being. This highlights the need for improved monitoring and guidelines on social media platforms to protect individuals from discriminatory and harmful actions. Despite extensive research on resource-rich languages, such as English and German, the detection and analysis of hate speech in less-resourced languages, such as Norwegian, remains underexplored. Addressing this gap, our study leverages a metalinguistic approach that uses advanced meta-learning techniques to enhance the detection capabilities across bilingual texts, effectively linking technical advancements directly to the pressing social issue of hate speech. In this study, we introduce techniques that adapt models that deal with hate speech detection within the same languages (intra-lingual), across different languages (cross-lingual), and techniques that adapt models to new languages with minimal extra training, independent of the model type (cross-lingual model-agnostic meta-learning-based approaches) for bilingual text analysis in Norwegian and English. Our methodology incorporates attention mechanisms (components that help the model focus on relevant parts of the text) and adaptive learning rate schedulers (tools that adjust the learning speed based on performance). Our methodology incorporates components that help the model focus on relevant parts of the text (attention mechanisms) and tools that adjust the learning speed based on performance (adaptive learning rate schedulers). We conducted various experiments using language-specific and multilingual transformers. Among these, the combination of Nor-BERT and LSTM with zero-shot and few-shot model-agnostic meta-learning achieved remarkable F1 scores of 79% and 90%, highlighting the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]

In most of the Indian low-resourced government primary schools, it is observed that a single teacher teaches across classes and subjects. Such multi-grade and multi-subject teaching are generally considered as one of the major challenges in primary education. However, it is believed that through translanguaging pedagogy these challenges can be converted to an advantage. In this context, this paper reports a study aimed to develop the 'knowledge-base' of government primary school teachers in Assam, one of the North-Eastern states in India. Multi-grade and multi-subject teaching sessions were observed to understand the current status of teaching reading and the potential of translanguaging pedagogy in reading instructions. Informal discussions were also used to get a richer picture of the reading and the possibility of translanguaging. Then, attempts were made to demonstrate how to infuse translanguaging into the structured reading lessons by using students' first language to create a translanguaging space in low-resourced classrooms, using bilingual texts, promoting flexible language use, and providing concurrent instructions in students' first language (Assamese in this context) and English. Postdemonstration informal discussions with the teachers revealed that these sessions helped in developing the teachers' knowledge-base. It is recommended that translanguaging can be included in teacher education programmes for primary school teachers working in multi-grade/subject contexts. [ABSTRACT FROM AUTHOR]

The bilingual-Latin-Greek-documents from the Byzantine imperial chancery provide insight into the work on translating texts with many technical terms in Constantinople. A unique source on translation studies are the treaties between the Byzantine emperor and Venice under the Palaiologan dynasty most of which are preserved in original form in the State Archive of Venice. This study outlines the problems these documents pose in their development and critically discusses the commonly held view on original Greek texts and respective Latin translations. Reflecting the process of negotiating a treaty, the translation runs from Latin to Greek at least in some cases. However, the ultimate result, a bilingual contract, depends on several parameters, so the vocabulary, grammar and any prior contracts and repeated phrases or sentences in each document need to be analysed; furthermore, one must determine whether a phrase originally came from Latin or Greek. The bilingual treaty resulting from a very complex process is termed here “complex translation”. This article approaches the problem through two case studies. It will be pivotal for further studies to compare the wording of the treaty items with the preceding ones. To facilitate a diachronic overview, the study ends with two lists of the addressed issues.

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This paper addresses the possibilities for further philological and linguistic studies of the Hattian text sources that will be feasible within the next few years. A digital searchable corpus of Hattian texts, in combination with a digital corpus of the Hittite texts, would open up new venues of research. These corpora will facilitate lexical, morphological and phonological studies as well as the reconstruction of text compositions with Hattian passages and of the tablets on which they were inscribed. It is hoped that the digital corpora will also aid in the potential identification of hitherto unrecognized correspondences between Hattian and Hittite texts. [ABSTRACT FROM AUTHOR]

Drawing on an earlier study that views CEO communication as an important strategic tool, this study analyzes the content of CEO messages on Web sites of major corporations in Greater China to reveal their extratextual and intratextual characteristics. The study suggests that the language style employed in these messages, including the linguistic characteristics, regional themes, and interlingual themes, is associated with a corporate communication strategy that is underpinned by CEOs’ beliefs and rooted in cultural values. The findings enhance our understanding of how CEOs view their stakeholders and the content that they include in their messages to stakeholders in order to compete in this digital age. [ABSTRACT FROM PUBLISHER]