The role of educational drama and the successful implementation
of drama techniques in teaching foreign languages have been confirmed by
various research findings. There is a growing need for English as a foreign
language (EFL) teachers to find appropriate tools to improve the teaching and
learning of EFL to satisfy learners with special educational needs (SEN). The
paper focuses on the link between the incorporation of drama techniques
and teaching learners with SEN in the higher level of primary school with
close attention to special needs connected with cognition and learning (e.g.
dyslexia, dysgraphia, etc.) and needs connected with social, emotional,
and mental health (e.g. ADD, ADHD, etc.). The paper presents the findings
of document analysis and semi-structured interviews with in-service EFL
teachers at the higher level of primary schools with Hungarian language of
instruction in Slovakia. The major aim of the interviews was to investigate
EFL teachers’ attitudes and self-efficacy in implementing inclusive
practices, their attitudes and experiences concerning teaching learners with
SEN and using drama techniques. A secondary aim was to examine the
current situation in schools and the support EFL teachers receive in their
institution. Based on the findings, implications for management strategies,
teacher education, and teacher training courses or trainings are discussed.
Flow is described as a state in which people become so involved or engrossed in an activity that nothing else seems to matter (Csikszentmihalyi, 2009). This state of consciousness seems to occur when a person is involved in a task and seemingly unable to stop. Flow states are marked by (a) a perceived balance of skills and challenge, (b) opportunities for intense concentration, (c) clear task goals, (d) feedback that one is succeeding at the task, (e) a sense of control, (f) a lack of self-consciousness, and (g) the perception that time passes more quickly (Egbert, 2003). The Japanese Flow State Scale (JFSS) is an instrument which was created specifically to measure flow states experienced during deliberate vocabulary study and is a working component of the first author's Doctor of Philosophy research project, which focuses on the deliberate study of vocabulary with augmented reality (AR) and physical word cards. Analyses with mixed effects models indicated that statistically significant differences in markers of states of flow elicited with the JFSS of 179 L1 Japanese participants on the basis of four deliberate vocabulary study activities (AR, word card study, Quizlet live, and intensive reading) appear to exist.
Petar Milovanovic, Tatjana Pekmezovic, Marija Djuric
Universities and journals increasingly rely on software tools for detecting textual overlap of a scientific text with the previously published literature to detect potential plagiarism. Although software outputs need to be carefully reviewed by competent humans to verify the existence of plagiarism, university and journal staff, for various reasons, often erroneously interpret the degree of plagiarism based on the percentage of textual overlap shown in the similarity report. This is often accompanied by explicit recommendations to the author(s) to paraphrase the text to achieve an “acceptable” percentage of overlap. Here, based on the available literature and real-world examples from similarity reports, we provide a classification with extensive examples of phrases that falsely inflate the similarity index and argue the futility and dangers of rephrasing such statements just for the sake of reducing the similarity index. The examples provided in this paper call for a more reasonable assessment of text similarity. To fully endorse the principles of academic integrity and prevent loss of clarity of the scientific literature, we believe it is important to shift from pure bureaucratic and quantificational view on the originality of scientific texts to human-centered qualitative assessment of the manuscripts, including the software outputs.
Communication. Mass media, Information resources (General)
Aligning acoustic and linguistic representations is a central challenge to bridge the pre-trained models in knowledge transfer for automatic speech recognition (ASR). This alignment is inherently structured and asymmetric: while multiple consecutive acoustic frames typically correspond to a single linguistic token (many-to-one), certain acoustic transition regions may relate to multiple adjacent tokens (one-to-many). Moreover, acoustic sequences often include frames with no linguistic counterpart, such as background noise or silence may lead to imbalanced matching conditions. In this work, we take a new insight to regard alignment and matching as a detection problem, where the goal is to identify meaningful correspondences with high precision and recall ensuring full coverage of linguistic tokens while flexibly handling redundant or noisy acoustic frames in transferring linguistic knowledge for ASR. Based on this new insight, we propose an unbalanced optimal transport-based alignment model that explicitly handles distributional mismatch and structural asymmetries with soft and partial matching between acoustic and linguistic modalities. Our method ensures that every linguistic token is grounded in at least one acoustic observation, while allowing for flexible, probabilistic mappings from acoustic to linguistic units. We evaluate our proposed model with experiments on an CTC-based ASR system with a pre-trained language model for knowledge transfer. Experimental results demonstrate the effectiveness of our approach in flexibly controlling degree of matching and hence to improve ASR performance.
Vision Language Navigation (VLN) typically requires agents to navigate to specified objects or remote regions in unknown scenes by obeying linguistic commands. Such tasks require organizing historical visual observations for linguistic grounding, which is critical for long-sequence navigational decisions. However, current agents suffer from overly detailed scene representation and ambiguous vision-language alignment, which weaken their comprehension of navigation-friendly high-level scene priors and easily lead to behaviors that violate linguistic commands. To tackle these issues, we propose a navigation policy by recursively summarizing along-the-way visual perceptions, which are adaptively aligned with commands to enhance linguistic grounding. In particular, by structurally modeling historical trajectories as compact neural grids, several Recursive Visual Imagination (RVI) techniques are proposed to motivate agents to focus on the regularity of visual transitions and semantic scene layouts, instead of dealing with misleading geometric details. Then, an Adaptive Linguistic Grounding (ALG) technique is proposed to align the learned situational memories with different linguistic components purposefully. Such fine-grained semantic matching facilitates the accurate anticipation of navigation actions and progress. Our navigation policy outperforms the state-of-the-art methods on the challenging VLN-CE and ObjectNav tasks, showing the superiority of our RVI and ALG techniques for VLN.
Social categories and stereotypes are embedded in language and can introduce data bias into Large Language Models (LLMs). Despite safeguards, these biases often persist in model behavior, potentially leading to representational harm in outputs. While sociolinguistic research provides valuable insights into the formation of stereotypes, NLP approaches for stereotype detection rarely draw on this foundation and often lack objectivity, precision, and interpretability. To fill this gap, in this work we propose a new approach that detects and quantifies the linguistic indicators of stereotypes in a sentence. We derive linguistic indicators from the Social Category and Stereotype Communication (SCSC) framework which indicate strong social category formulation and stereotyping in language, and use them to build a categorization scheme. To automate this approach, we instruct different LLMs using in-context learning to apply the approach to a sentence, where the LLM examines the linguistic properties and provides a basis for a fine-grained assessment. Based on an empirical evaluation of the importance of different linguistic indicators, we learn a scoring function that measures the linguistic indicators of a stereotype. Our annotations of stereotyped sentences show that these indicators are present in these sentences and explain the strength of a stereotype. In terms of model performance, our results show that the models generally perform well in detecting and classifying linguistic indicators of category labels used to denote a category, but sometimes struggle to correctly evaluate the associated behaviors and characteristics. Using more few-shot examples within the prompts, significantly improves performance. Model performance increases with size, as Llama-3.3-70B-Instruct and GPT-4 achieve comparable results that surpass those of Mixtral-8x7B-Instruct, GPT-4-mini and Llama-3.1-8B-Instruct.
The aim of the study is to identify points of contact between linguistic and psychoanalytic discourses and metadiscourses. The scientific novelty of the work consists in the construction of a linguo-semiological model for describing discourse and metadiscourse in order to clarify the conceptual apparatus and more clearly structure the semantic terminology of linguistics, psychoanalysis, as well as the theory of language and communication theory. An important point of the study is the discovery of epistemological similarities and differences between the terminological apparatus and operational models of text analysis used in both disciplines, in other words, between the linguistic and psychoanalytic processing of utterances in line with the interpretative-semiological approach. The obtained data allow us to identify several crucial mechanisms of linguistic and semiotic interaction between the subjects of various scientific fields. This synergy is essential for effective communication and drives the advancement of linguistic and cultural knowledge. By activating the dynamics of linguistic and cultural data, we can create a new metadiscourse axiology in both theory and practice. This applies not only to the field of psycholinguistics but also to general linguistics.
In the current higher education context, the development of academics’ competencies seems to be a crucial issue, with a strong focus on teaching, learning and assessment digital skills (Redecker & Punie, 2017). In connection with the framework of DigCompEdu (2017), it seems important to understand how to better sustain academics’ new professionalisation as Digital Scholars (Weller, 2011) in order to structure efficient and effective academic development models aimed at fostering new teaching skills required at university in the post pandemic era.
Głównym celem artykułu jest sklasyfikowanie i omówienie mechanizmów kreowania postprawdy w przestrzeni medialnej. Na wybranych przykładach scharakteryzuję i opiszę funkcjonowanie 10 mechanizmów, które wyróżniłem w trakcie prowadzonych badań. Podzieliłem je na mechanizmy związane z działaniami podjętymi przez nadawcę oraz wynikające z jego zaniechania. Ułatwi to zrozumienie, w jaki sposób te techniki są wykorzystywane w mediach oraz że kreowanie postprawdziwych komunikatów wcale nie musi być intencjonalne. Termin „postprawda” porównam ponadto z takimi pojęciami jak: manipulacja, zwodzenie (ang. deception) czy „wciskanie kitu” (ang. bullshit), chcąc odnaleźć między nimipunkty wspólne i różnice, co pozwoli zrozumieć złożoność problemu również na gruncie semantycznym.
Language models (LMs) may lead their users to make suboptimal downstream decisions when they confidently hallucinate. This issue can be mitigated by having the LM verbally convey the probability that its claims are correct, but existing models cannot produce long-form text with calibrated confidence statements. Through the lens of decision-making, we define linguistic calibration for long-form generations: an LM is linguistically calibrated if its generations enable its users to make calibrated probabilistic predictions. This definition enables a training framework where a supervised finetuning step bootstraps an LM to emit long-form generations with confidence statements such as "I estimate a 30% chance of..." or "I am certain that...", followed by a reinforcement learning step which rewards generations that enable a user to provide calibrated answers to related questions. We linguistically calibrate Llama 2 7B and find in automated and human evaluations of long-form generations that it is significantly more calibrated than strong finetuned factuality baselines with comparable accuracy. These findings generalize under significant domain shifts to scientific and biomedical questions and to an entirely held-out person biography generation task. Our results demonstrate that long-form generations may be calibrated end-to-end by constructing an objective in the space of the predictions that users make in downstream decision-making.
Aim. The paper aims to trace the linguistic understanding of the notion “genre” and genre typology of scientific texts in Russian linguistics of the 1960s – 1970s.Methodology. The research is based on the analysis of linguistics studies on genre in general and the genres of scientific texts in particular. The study is conducted through observation, generalization, interpretation of the results and comparative analysis.Results. It was found out that linguistic thought passed several stages on the way to developing genre approach to scientific texts and that it was possible due to its collaboration with other disciplines, such as literary studies, theory of translation, language teaching theory and book studies, the whole process having been guided by the logic of the development of science itself, especially information science.Research implications. The obtained results may broaden the horizons of scientific text studies from genre perspective.
Extant research addressing the relations between TikTok videos and sustainable apparel consumption behavior is limited. This study explores these relations by testing the following theories and constructs: social consciousness, prior sustainable apparel purchasing, attitude toward TikTok videos (featuring sustainable apparel content), and theory of planned behavior. Results from an online survey supported the proposed conceptual framework, suggesting that cognitive, affective, and behavioral factors relevant to sustainable apparel consumption had a positive influence on sustainable apparel purchase intention.
Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which Verbal and Nominal are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.
Language change is influenced by many factors, but often starts from synchronic variation, where multiple linguistic patterns or forms coexist, or where different speech communities use language in increasingly different ways. Besides regional or economic reasons, communities may form and segregate based on political alignment. The latter, referred to as political polarization, is of growing societal concern across the world. Here we map and quantify linguistic divergence across the partisan left-right divide in the United States, using social media data. We develop a general methodology to delineate (social) media users by their political preference, based on which (potentially biased) news media accounts they do and do not follow on a given platform. Our data consists of 1.5M short posts by 10k users (about 20M words) from the social media platform Twitter (now "X"). Delineating this sample involved mining the platform for the lists of followers (n=422M) of 72 large news media accounts. We quantify divergence in topics of conversation and word frequencies, messaging sentiment, and lexical semantics of words and emoji. We find signs of linguistic divergence across all these aspects, especially in topics and themes of conversation, in line with previous research. While US American English remains largely intelligible within its large speech community, our findings point at areas where miscommunication may eventually arise given ongoing polarization and therefore potential linguistic divergence. Our methodology - combining data mining, lexicostatistics, machine learning, large language models and a systematic human annotation approach - is largely language and platform agnostic. In other words, while we focus here on US political divides and US English, the same approach is applicable to other countries, languages, and social media platforms.
Uri Berger, Lea Frermann, Gabriel Stanovsky
et al.
We present a large, multilingual study into how vision constrains linguistic choice, covering four languages and five linguistic properties, such as verb transitivity or use of numerals. We propose a novel method that leverages existing corpora of images with captions written by native speakers, and apply it to nine corpora, comprising 600k images and 3M captions. We study the relation between visual input and linguistic choices by training classifiers to predict the probability of expressing a property from raw images, and find evidence supporting the claim that linguistic properties are constrained by visual context across languages. We complement this investigation with a corpus study, taking the test case of numerals. Specifically, we use existing annotations (number or type of objects) to investigate the effect of different visual conditions on the use of numeral expressions in captions, and show that similar patterns emerge across languages. Our methods and findings both confirm and extend existing research in the cognitive literature. We additionally discuss possible applications for language generation.
Este artigo representa uma parte das descobertas da pesquisa de doutorado acerca da relação entre gravidez e consumo. O que apresento aqui tem por objetivo discutir o imperativo da visibilidade1 como condição de existência na contemporaneidade. Nesse contexto, as gestações imbricadas pelo consumo fazem com que os bebês nasçam mesmo antes de seus partos. Os bebês existem como imagens, mediados também por objetos e midiatizados em suas vidas intrauterinas. A jornada seriada e digitalizada da gravidez é, ao mesmo tempo, elemento de pedagogização de consumo para um modelo de gestação e também objeto de entretenimento. Qualquer semelhança com o filme O show de Truman não nos parece mera coincidência. Tais descobertas foram possíveis por meio de metodologia que contou com pesquisa de inspiração etnográfica, em visitas aos quartos dos bebês em gestação; monitoramento de hashtags em redes sociais (Instagram e Facebook); e busca das mesmas palavras-chave em vídeos do YouTube. Para dar conta da interpretação do fenômeno analisado, são mobilizados os seguintes conceitos e bases teóricas: mediação, midiatização, visibilidade e estetização
Communication. Mass media, Social sciences (General)
The past years have witnessed the collection of various corpora for the study of Irish English (IE). Most have been developed and driven by diverse research foci with a specific aim in mind, however, at present data sources consist of unavailable and/or outdated audio files of English spoken primarily in Dublin and Belfast. Additionally, a very limited number of investigations on the prosodic features of IE varieties have been conducted to date. As a result, a comprehensive overview of IE prosodic diversity is still missing and existing speech corpora do not allow for the analysis of intonation patterns, which requires more controlled, purpose-built data sets. A prosodic corpus devoted to the analysis of IE varieties needs to be incorporated into the research agenda. This contribution presents the corpus of Irish English Speech (IES) with the following objectives: to collect recordings of spoken IE across present-day Ireland under a unified protocol in order to guarantee comparisons among different datasets; to obtain an initial phonological inventory of each variety examined; to compare the phonological systems of diverse IE varieties; and to provide researchers with accessible and open data sources. The core of the corpus has been gathered in accordance with the guidelines of the Interactive Atlas of Romance Intonation project (Prieto, Borràs-Comes & Roseano, 2011-2014) via a questionnaire based on the Discourse Completion Task, which was translated and readapted for Irish English speakers, and a Map Dialogue Task designed to obtain spontaneous speech productions. This method has yielded the collection of a wide range of intonation patterns concerning different types of context-specific utterances, such as statements, questions, imperatives and vocatives. After an overview of the segmental phonology of IE, previous studies on the prosodic features of IE varieties and the speech corpora of IE will be examined with the purpose of identifying the gaps in existing literature, which will then be followed by a detailed outline of the development of the corpus of IES. This contribution will provide an illustrative example for fully exploiting the potential of the IES database and call for further in-depth investigations on IE prosody.
This is the Festschrift of Dr. Jack Rueter. The book presents peer-reviewed scientific work from Dr. Rueter’s colleagues related to the latest advances in natural language processing, digital resources and endangered languages in a variety of languages such as historical English, Chukchi, Mansi, Erzya, Komi, Finnish, Apurina, Sign Languages, Sami languages and Japanese. Most of the papers present work on endangered languages or on domains with a limited number of resources available for NLP. This book collects original and insightful papers from well-established researchers in NLP, linguistics, philology and digital humanities. This is a tribute to Dr. Rueter’s long career that is characterized by constant altruistic work towards a greater good in building free and open-source tools and resources for endangered languages. Dr. Rueter is a true pioneer in the field of digital documentation of endangered languages.