Hasil untuk "Language and Literature"

Phil Hiver, Ali H. Al-Hoorie, Joseph P. Vitta et al.

At the turn of the new millennium, in an article published in Language Teaching Research in 2000, Dörnyei and Kormos proposed that ‘active learner engagement is a key concern’ for all instructed language learning. Since then, language engagement research has increased exponentially. In this article, we present a systematic review of 20 years of language engagement research. To ensure robust coverage, we searched 21 major journals on second language acquisition (SLA) and applied linguistics and identified 112 reports satisfying our inclusion criteria. The results of our analysis of these reports highlighted the adoption of heterogeneous methods and conceptual frameworks in the language engagement literature, as well as indicating a need to refine the definitions and operationalizations of engagement in both quantitative and qualitative research. Based on these findings, we attempted to clarify some lingering ambiguity around fundamental definitions, and to more clearly delineate the scope and target of language engagement research. We also discuss future avenues to further advance understanding of the nature, mechanisms, and outcomes resulting from engagement in language learning.

Lane Gormley

K. Burke

L. Thomas, Shân Wareing

Maurice Blanchot

Maurice Blanchot, the eminent literary and cultural critic, has had a vast influence on contemporary French writers--among them Jean Paul Sartre and Jacques Derrida. From the 1930s through the present day, his writings have been shaping the international literary consciousness."The Space of Literature," first published in France in 1955, is central to the development of Blanchot's thought. In it he reflects on literature and the unique demand it makes upon our attention. Thus he explores the process of reading as well as the nature of artistic creativity, all the while considering the relation of the literary work to time, to history, and to death. This book consists not so much in the application of a critical method or the demonstration of a theory of literature as in a patiently deliberate meditation upon the literary experience, informed most notably by studies of Mallarme, Kafka, Rilke, and Holderlin. Blanchot's discussions of those writers are among the finest in any language.

N. Ardoin, A. Bowers

Environmental education focused on the early-childhood years is experiencing dynamic growth in research and practice due to persistent environmental challenges coupled with burgeoning interest in the documented benefits of nature-rich experiences for infants and children. To better understand the landscape of early childhood environmental education (ECEE) pedagogical practices and expected outcomes, we undertook a systematic review of empirical studies of ECEE programs. Focusing on a 25-year span, we surfaced 66 studies that met our inclusion criteria. We found that participants in such programs spanned the early-childhood age range (birth through age eight) with the majority involving three- to six-year-olds in teacher-led, formal (school-like) programs. The primary outcomes documented in our sample studies included environmental literacy development, cognitive development, and social and emotional development. To a lesser extent, the studies addressed physical development and language and literacy development. On balance, our sample of ECEE studies reported strongly positive findings associated with the aforementioned outcomes. The majority emphasized the effectiveness of play-based, nature-rich pedagogical approaches that incorporated movement and social interaction. We include a visualization that synthesizes cross-sample findings with the intention of assisting ECEE practitioners in developing, implementing, and evaluating programs as well as encouraging researchers to further study elements, processes, and theoretical assumptions inherent in them.

E. Felten, Manav Raj, Robert C. Seamans

Recent dramatic increases in AI language modeling capabilities has led to many questions about the effect of these technologies on the economy. In this paper we present a methodology to systematically assess the extent to which occupations, industries and geographies are exposed to advances in AI language modeling capabilities. We find that the top occupations exposed to language modeling include telemarketers and a variety of post-secondary teachers such as English language and literature, foreign language and literature, and history teachers. We find the top industries exposed to advances in language modeling are legal services and securities, commodities, and investments. We also find a positive correlation between wages and exposure to AI language modeling.

Jeong-Bae Son, N. K. Ružić, A. Philpott

Abstract Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education, and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

Roxana Rebolledo Font de la Vall, Fabián González Araya

AI language learning tools are computer programs or software applications that use artificial intelligence (AI) algorithms to help users learn and improve their skills in a foreign language. These technologies provide a variety of advantages, including the capacity to reduce time and increase learning speed, to give learners personalized learning experiences, and to introduce them to other cultures. In this study, scientific literature has been reviewed in order to analyze opportunities, challenges and limitations to using AI language learning tools, including the need for more human interaction, and contextual nuances of language, and dependence on large amounts of data for training.  These AI tools have several potential future advances, including integrating VR and AR technologies, improving natural language processing algorithms, and developing more advanced adaptive learning algorithms. By addressing these issues and constraints, AI learning systems have the potential to become even more powerful and impactful tools for language learning, and their integration plays a crucial role in designing more effective and efficient learning user experience solutions.

Liss Jenneboer, C. Herrando, Efthymios Constantinides

More and more companies have implemented chatbots on their websites to provide support to their visitors on a 24/7 basis. The new customer wants to spend less and less time and therefore expects to reach a company anytime and anywhere, regardless of time, location, and channel. This study provides insight into the influence of chatbots on customer loyalty. System quality, service quality, and information quality are crucial dimensions that a chatbot must meet to give a good customer experience. To make a chatbot more personal, companies can alter the language style. Human-like chatbots lead to greater satisfaction and trust among customers, leading to greater adoption of the chatbot. The results of this study showed that a connection between chatbots and customer loyalty is very likely. Besides, some customers suffer from the privacy paradox because of personalization. Implications of this study are discussed.

M. Ulla, W. Perales, S. Busbus

ABSTRACT The present research explores the perspectives of English as a foreign language (EFL) teachers at a Thai university regarding ChatGPT as a language teaching tool. Seventeen EFL teachers completed an online interview survey, while three of these teachers engaged in a subsequent individual interview in person. Utilising a qualitative descriptive research paradigm, the results indicate that the participants exhibited positive attitudes towards ChatGPT and acknowledged its diverse applications, including lesson preparation and language activity creation. The participants also highlighted the limitations of ChatGPT, particularly in its dependability, trustworthiness, and capacity to promote excessive student dependence. This study makes a valuable contribution to the extant literature by illuminating the potential benefits and drawbacks of utilising ChatGPT in language education.

Clara-Louise Mourier

Jean Hegland’s Into the Forest (1996) concludes with two sisters and their infant Burl abandoning their family home to embrace life in a neighbouring forest. Western domesticity, symbolised by private bedrooms, is replaced by the plural, biotic community of a centuries-old redwood forest. By taking refuge in a hollow stump, the trio attempts to shed their now obsolete social identity and adapt to a postapocalyptic American West. This process of redrawing the border between inhabited and uninhabitable spaces continues twenty years later in Here in This Next New Now (in French, Le Temps d’après). Burl, now a non-binary “arboreal boy,” undertakes to recount their life within an ecosystem saturated by the non-human. Yet, the character resents their entrapment in the hollow stump chosen by their mothers for protection. Instead, they attempt to reconfigure the entire forest (and beyond) into a potential shelter for human and non-human life. The figure of the bed-stump thus evolves into that of the encampment. In exploring the bed and its redefinitions, this study not only traces the reintegration of the characters into a multispecies world, but also invites readers to consider Hegland’s narrative practices as a refusal to enclose the text in a fixed or stable cartography. On the contrary, the novel ultimately overflows the book as a medium.

Uli Sauerland, Celia Matthaei, Felix Salfner

We argue that human language learning proceeds in a manner that is different in nature from current approaches to training LLMs, predicting a difference in learning biases. We then present evidence from German plural formation by LLMs that confirm our hypothesis that even very powerful implementations produce results that miss aspects of the logic inherent to language that humans have no problem with. We conclude that attention to the different structures of human language and artificial neural networks is likely to be an avenue to improve LLM performance.

John Pavlopoulos, Juli Bakagianni, Kanella Pouli et al.

Natural Language Processing (NLP) for lesser-resourced languages faces persistent challenges, including limited datasets, inherited biases from high-resource languages, and the need for domain-specific solutions. This study addresses these gaps for Modern Greek through three key contributions. First, we evaluate the performance of open-source (Llama-70b) and closed-source (GPT-4o mini) large language models (LLMs) on seven core NLP tasks with dataset availability, revealing task-specific strengths, weaknesses, and parity in their performance. Second, we expand the scope of Greek NLP by reframing Authorship Attribution as a tool to assess potential data usage by LLMs in pre-training, with high 0-shot accuracy suggesting ethical implications for data provenance. Third, we showcase a legal NLP case study, where a Summarize, Translate, and Embed (STE) methodology outperforms the traditional TF-IDF approach for clustering \emph{long} legal texts. Together, these contributions provide a roadmap to advance NLP in lesser-resourced languages, bridging gaps in model evaluation, task innovation, and real-world impact.

Kevin Hunt

This article addresses the fundamental concept underpinning Jonathan Glazer's The Zone of Interest, which recognizes selective empathy and extraordinary empathy dissonance within our contemporary cultures as a continuum, not a moment. The article uses Michel Serres's philosophical process to provide an ontological and epistemological framework within which The Zone of Interest can be understood analogously as a warning about darkness enveloping the world. Glazer has emphasized the axiom of his film is focusing upon the present. The Zone of Interest asks questions about humanity's contemporary cultural sensibilities, which determine how societies engage with diversity, difference, and the multiplicities of perspective that are an inescapable part of the global geopolitical landscape. Serres's process is inherently analogical, recognizing patterns of knowing and being that recur isomorphically across space and time. This article brings together the immersive sensibility mediated through the screen – situating The Zone of Interest as a cinematic experience that elevates sound over vision – with Serres's assimilation of Lucretian atomism, which links materialism and ethics; the importance of noise as a source of knowledge within Serresian thought; and a topological approach to time and space, which shapes the analogical, qualitatively relational, processes characteristic of Serres's philosophy.

M. Guerra-Martín, María Del Rocío Casado-Espinosa, Yelena Gavira-López et al.

(1) Background: Cancer constitutes one of the principal causes of morbi-mortality in the world and generates an important loss of patients’ self-sufficiency. People who are their caregivers usually become the main care providers, which impacts their quality of life; (2) Aim: Analyze the different problems (physical, emotional, social, and financial) faced by people who are caregivers of adults with cancer and describe the strategies required to improve their quality of life; (3) Method: A literature review was conducted on the following database: PubMed, Cinahl, PsycINFO, and Scopus. The following eligibility criteria were specified: (a) research studies of quantitative, qualitative, or mixed methods, (b) consistent with objective, and (c) published in the English language or Spanish during the last five years; (4) Results: 36 studies were selected from those found in the literature. Regarding the problems mentioned: eight studies described physical issues, 26 emotional effects, 10 social implications, and seven financial strains. Twenty-eight studies described strategies to improve the quality of life of caregivers; (5) Conclusions: Caregivers are usually women around the age of 50. Problems faced are mostly emotional in nature, followed by social, physical, and financial ones. In order to cope with this burden, there are some strategies that can be developed to help to build skills to manage both the disease and the impact derived from it, therefore improving their quality of life.

Niyati Bafna, Kenton Murray, David Yarowsky

While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.

Xiaonan Wang, Jinyoung Yeo, Joon-Ho Lim et al.

Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.

Ziqiao Ma, Zekun Wang, Joyce Chai

Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. In this work, we explore how corrective feedback from interactions influences neural language acquisition from scratch through systematically controlled experiments, assessing whether it contributes to word learning efficiency in language models. We introduce a trial-and-demonstration (TnD) learning framework that incorporates three distinct components: student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal and smaller numbers of parameters, and we highlight the significance of both trials and demonstrations. We further show that the teacher's choices of words influence students' word-specific learning efficiency, and a practice-makes-perfect effect is evident by a strong correlation between the frequency of words in trials and their respective learning curves. Our findings suggest that interactive language learning, with teacher demonstrations and active trials, can facilitate efficient word learning in language models.

Tianchi Liu, Ivan Kukanov, Zihan Pan et al.

The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited. Existing anti-spoofing datasets are mainly in English, and the high cost of acquiring multilingual datasets hinders training language-independent models. We initiate this work by evaluating top-performing speech anti-spoofing systems that are trained on English data but tested on other languages, observing notable performance declines. We propose an innovative approach - Accent-based data expansion via TTS (ACCENT), which introduces diverse linguistic knowledge to monolingual-trained models, improving their cross-lingual capabilities. We conduct experiments on a large-scale dataset consisting of over 3 million samples, including 1.8 million training samples and nearly 1.2 million testing samples across 12 languages. The language mismatch effects are preliminarily quantified and remarkably reduced over 15% by applying the proposed ACCENT. This easily implementable method shows promise for multilingual and low-resource language scenarios.