Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

Critical Praxis Through a Social Media Ecosystem

The use of social media in language education is evident in the plethora of online content generated by education organizations. Teachers and learners alike have used platforms such as Facebook, YouTube, and Instagram to access and disseminate learning content in the forms of text, images, podcasts, and videos. However, despite the prevalence of social media in the language-learning sector, its pedagogical use has been limited to learning language features. This chapter analyzes the potential use of an ecosystem of social media platforms to augment varied modes of TESOL instruction, namely live, online, and hybrid, through a critical lens in higher and adult education. The integration of critical content and critical thinking development in social media platforms, in which authentic content is directly consumed, co-created, and disseminated, enables TESOL teachers to help learners become aware of how power shapes information, how to resist coercion, and challenge the status quo.

ROLA CZYNNIKÓW AFEKTYWNYCH NA PRZYKŁADZIE STREFY KOMFORTU NA ZDALNYCH ZAJĘCIACH JĘZYKOWYCH

Affective factors are one of the two types of individual factors that influence success in the process of foreign language learning. They consist of personality traits, as well as positive and negative emotions. This emotional dichotomy is also reflected in the popular concept of comfort zone. The aim of this paper is to define the comfort zone in the context of distance learning language classes and to check if and how it is possible to implement this model in research in the field of glottodidactics. The results of empirical research on the perception and experience of the comfort zone during synchronous distance learning classes by philology students are presented and analyzed in order to draw conclusions on the impact of positive and negative emotions on the process and effects of the language distance learning.

A Review on Semantic Waves and Its Application in Discourse Analysis

Since the past 10 years, the theory of semantic waves has further progressed. This theory is deeply rooted in the theory of knowledge structures, legitimation code theory, and systemic functional linguistics. In addition, the theory can also be applied in discourse analysis, language learning, language teaching, and many other fields.

Jezik in turizem, Language and Tourism, Sprache und Tourismus

In the monograph ten scientific chapters oriented towards language for tourism that span from language learning and teaching, to lexicography, minority languages, and selected linguistic concepts are presented. Among them is the analyses of the features of the Slovene LSP Dictionary of Tourism, the question of minority communities and their tourism websites, the collocation strength and contrastive analyses of adjective-noun collocations, the concept of movement in tertiary education, the analyses of Slovene –German translations of chosen online menus, the tourist web resources as part of the L2 classroom, the connection of linguistic landscapes with tourism, writing skills in English for Tourism, local language variants of personal names, and teaching and learning language for special purposes during the COVID-19 pandemic.

Language Models for the Prediction of SARS-CoV-2 Inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ~9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Outsiders in current Slovak literature for children and youth

Within the solution of the project APVV-17-0071 Support of Reading Literacy in the Mother Tongue and Foreign Language, it is also important to reflect on outsidership as a certain ambivalent phenomenon, which appears after 1989 in contemporary Slovak literature for children and youth in various analogies. One of the aims of the study is to define the initial concept of outsider from various professional perspectives. We will also focus on the methodological basis of research of outsiders (social status, otherness, disadvantage, bullying, rebellion), not only in contemporary artistic texts but also in working exercises with regard to the learning language style.

Semantic programming by example with pre-trained models

The ability to learn programs from few examples is a powerful technology with disruptive applications in many domains, as it allows users to automate repetitive tasks in an intuitive way. Existing frameworks on inductive synthesis only perform syntactic manipulations, where they rely on the syntactic structure of the given examples and not their meaning. Any semantic manipulations, such as transforming dates, have to be manually encoded by the designer of the inductive programming framework. Recent advances in large language models have shown these models to be very adept at performing semantic transformations of its input by simply providing a few examples of the task at hand. When it comes to syntactic transformations, however, these models are limited in their expressive power. In this paper, we propose a novel framework for integrating inductive synthesis with few-shot learning language models to combine the strength of these two popular technologies. In particular, the inductive synthesis is tasked with breaking down the problem in smaller subproblems, among which those that cannot be solved syntactically are passed to the language model. We formalize three semantic operators that can be integrated with inductive synthesizers. To minimize invoking expensive semantic operators during learning, we introduce a novel deferred query execution algorithm that considers the operators to be oracles during learning. We evaluate our approach in the domain of string transformations: the combination methodology can automate tasks that cannot be handled using either technologies by themselves. Finally, we demonstrate the generality of our approach via a case study in the domain of string profiling.