Hasil untuk "Language and Literature"

H. Hughes

J. Chahla, Mark E. Cinque, N. Piuzzi et al.

A. Parmaxi

ABSTRACT The increasing popularity of Virtual Reality (VR) has provoked scholars’ and educators’ interest to explore its potential as a learning environment for various fields of education. Along this line, several literature reviews have analysed and synthesised the educational use of VR; however, scholar activity is lacking a recent review of VR on a specific field of interest such as language learning. Thus this paper delineates the contour of scholarly literature on VR as an emerging technology in language teaching and learning. Using 17 high-impact journals and conferences in the fields of Computer-Assisted Language Learning and Educational Technology as a source, 26 scholarly manuscripts were retrieved from 2015 to 2018, analysed and synthesised under the following foci: (a) technologies used, language learning settings and duration of educational activities; (b) benefits and limitations from using VR as an educational tool in the language classroom; (c) future research directions regarding the educational use of VR based on the reviewed literature. This paper argues that VR is an invaluable tool in the language classrooms but entails challenges regarding its technical configuration, as well as its pedagogical grounding. The study concludes with some discussion and implications for researchers and practitioners.

Chengchen Li

ABSTRACT The influence of (trait) emotional intelligence (TEI) on academic achievement has been documented in literature, while its influence in the specific domain of L2 learning remains underexplored. The link between EI and negative emotions especially anxiety has been studied in different contexts including applied linguistics. However, it remains unknown how EI is related to positive emotions in L2 learning. Underpinned by theories and assumptions of Positive Psychology, the present study examined the complex relationships between 1307 Chinese high school students’ TEI, Foreign Language Enjoyment (FLE), and English-as-a-foreign-language (EFL) learning achievement. The following findings were obtained: (1) Most Chinese high school EFL students reported moderate to high levels of TEI, while low to moderate levels of FLE; (2) Small to medium correlations were found among students’ TEI, FLE, self-perceived English achievement and actual English achievement; (3) TEI was partially mediated by FLE to influence perceived achievement and actual achievement indirectly. The results were discussed in accordance with previous relevant findings as well as their theoretical and practical implications for L2 teaching and learning.

Ruofei Zhang, D. Zou

Abstract Digital technologies have been widely used to enhance language learning, the effectiveness of which has been acknowledged in the literature. With the rapid development of digital devices and technologies, increasing technologies have been used in the most recent several years, leading to more diversified approaches to language education. This exceptional advance over the past few years calls for a summary of state-of-the-art technologies that have been used to enhance language learning and promote effective learning. The present study was conducted to fill this gap by reviewing all the relevant publications in 10 widely recognised journals in the field of technology-enhanced language learning. An analysis of 57 articles indicated five major types of technology for second and foreign language learning (i.e. technologies for mobile learning, multimedia learning and socialisation, speech-to-text and text-to-speech recognition, and digital-game-based learning). The results also showed four primary purposes and benefits of the state-of-the-art technologies: promoting practices, delivering instructional content, facilitating interactions, and restructuring teaching approaches. Moreover, these state-of-the-art technologies have been integrated into various aspects of language teaching and learning, the overall impact of which has been positive.

Kaj Bostrom, Greg Durrett

The success of pretrained transformer language models (LMs) in natural language processing has led to a wide range of pretraining setups. In particular, these models employ a variety of subword tokenization methods, most notably byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994), the WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling (Kudo, 2018), to segment text. However, to the best of our knowledge, the literature does not contain a direct evaluation of the impact of tokenization on language model pretraining. We analyze differences between BPE and unigram LM tokenization, finding that the latter method recovers subword units that align more closely with morphology and avoids problems stemming from BPE’s greedy construction procedure. We then compare the fine-tuned task performance of identical transformer masked language models pretrained with these tokenizations. Across downstream tasks and two languages (English and Japanese), we find that the unigram LM tokenization method matches or outperforms BPE. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.

A. Dobrescu, Streit B Nussbaumer, I. Klerings et al.

Dan Amiram, Zahn Bozanic, James D. Cox et al.

Jian Huang, J. Chai, Stella Cho

Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Tareq Al-Moslmi, Marc Gallofré Ocaña, Andreas L. Opdahl et al.

An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other’s context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.

Jinghan Cao, Yu Ma, Xinjin Li et al.

Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficiency Ratio (PER), a novel metric integrating accuracy, throughput, memory, and latency through geometric mean normalization. Our systematic evaluation reveals that small models (0.5--3B parameters) achieve superior PER scores across all given tasks. These findings establish quantitative foundations for deploying small models in production environments prioritizing inference efficiency over marginal accuracy gains.

Mohammad Hossein Arefian

Language teacher education programs can become more reflective, inclusive, collaborative, situated, and inquiry-based. One such professional approach to incorporate these characters can be through personalized language teacher education (PLTE). Due to the importance of using AI and professional learning communities (PLCs) for developing a personalized teacher education, this study explored how AI-enhanced PLCs could be leveraged to create a more responsive, inclusive, and personalized teacher education. Still, a significant gap exists in understanding how AI can be specially integrated into PLCs to create personalized pathways for ELT preservice teachers, mainly in under-resourced contexts. To conduct this exploratory case study, 8 Iranian English language teaching (ELT) pre-service teachers were purposively selected from a teacher education university. Data was collected from group discussion, artifacts, and interviews, and the result of the thematic analysis revealed that AI-enhanced PLCs fostered personalized, reflective, and collaborative development by addressing individual teaching needs and providing innovative strategies. By addressing individual teaching needs and providing innovative instructional strategies, AI facilitated a dynamic learning environment. However, effective integration required overcoming challenges like limited AI literacy and contextual mismatches, highlighting the potential for tailored, impactful education. This study can inform teacher educators, policymakers, administrators, and teachers to integrate AI into their PLCs to develop a PLTE.

Marco Antônio Calijorne Soares, Fernando Silva Parreiras

Abstract Background Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. Objective This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to determine how the relationship between Question Answering and natural language processing is built. Method The method adopted was a Systematic Literature Review of studies published from 2000 to 2017. Results 130 out of 1842 papers have been identified as describing a QA approach developed and evaluated with different techniques. Conclusion Question Answering researchers have concentrated their efforts in natural language processing, knowledge base and information retrieval paradigms. Most of the researches focused on open domain. Regarding the metrics used to evaluate the approaches, Precision and Recall are the most addressed.

W. Albiladi, Khlood K. Alshareef

This paper provides a review of the research related to the use of blended learning in English as a second/foreign language context. Blended learning is a relatively new field that combines traditional teaching approaches with distance and online learning. The use of blended learning has been emphasized by the recent research that examines the academic and social benefits of this teaching approach. Because it combines traditional and online teaching modes, the promise of blended learning rests on the strengths of both teaching approaches. The present review of the literature revealed that blended learning can be used effectively to develop language skills, enhance the English learning environment, and promote students’ motivation toward learning the language. There is a dearth of literature that examines the challenges that face language teachers when using blended learning. Hence, more research has to be done to identify and deal with these challenges.

Anh Nguyen, Stefan Lee

To interact effectively with humans in the real world, it is important for agents to understand language that describes the dynamics of the environment--that is, how the environment behaves--rather than just task instructions specifying "what to do". Understanding this dynamics-descriptive language is important for human-agent interaction and agent behavior. Recent work address this problem using a model-based approach: language is incorporated into a world model, which is then used to learn a behavior policy. However, these existing methods either do not demonstrate policy generalization to unseen games or rely on limiting assumptions. For instance, assuming that the latency induced by inference-time planning is tolerable for the target task or expert demonstrations are available. Expanding on this line of research, we focus on improving policy generalization from a language-conditioned world model while dropping these assumptions. We propose a model-based reinforcement learning approach, where a language-conditioned world model is trained through interaction with the environment, and a policy is learned from this model--without planning or expert demonstrations. Our method proposes Language-aware Encoder for Dreamer World Model (LED-WM) built on top of DreamerV3. LED-WM features an observation encoder that uses an attention mechanism to explicitly ground language descriptions to entities in the observation. We show that policies trained with LED-WM generalize more effectively to unseen games described by novel dynamics and language compared to other baselines in several settings in two environments: MESSENGER and MESSENGER-WM.To highlight how the policy can leverage the trained world model before real-world deployment, we demonstrate the policy can be improved through fine-tuning on synthetic test trajectories generated by the world model.

Elias-Leander Ahlers, Witold Brunsmann, Malte Schilling

Assessing language proficiency is essential for education, as it enables instruction tailored to learners needs. This paper investigates the use of Large Language Models (LLMs) for automatically classifying German texts according to the Common European Framework of Reference for Languages (CEFR) into different proficiency levels. To support robust training and evaluation, we construct a diverse dataset by combining multiple existing CEFR-annotated corpora with synthetic data. We then evaluate prompt-engineering strategies, fine-tuning of a LLaMA-3-8B-Instruct model and a probing-based approach that utilizes the internal neural state of the LLM for classification. Our results show a consistent performance improvement over prior methods, highlighting the potential of LLMs for reliable and scalable CEFR classification.

Kristin Gnadt, David Thulke, Simone Kopeinik et al.

In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods initially developed for the English language when applied to other languages. This work aims to contribute to this research strand by presenting five German datasets for gender bias evaluation in LLMs. The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies. Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German, including the ambiguous interpretation of male occupational terms and the influence of seemingly neutral nouns on gender perception. This work contributes to the understanding of gender bias in LLMs across languages and underscores the necessity for tailored evaluation frameworks.

Aarush Sinha, Viraj Virk, Dipshikha Chakraborty et al.

Language Models [LMs] are now playing an increasingly large role in information generation and synthesis; the representation of scientific knowledge in these systems needs to be highly accurate. A prime challenge is hallucination; that is, generating apparently plausible but actually false information, including invented citations and nonexistent research papers. This kind of inaccuracy is dangerous in all the domains that require high levels of factual correctness, such as academia and education. This work presents a pipeline for evaluating the frequency with which language models hallucinate in generating responses in the scientific literature. We propose ArxEval, an evaluation pipeline with two tasks using ArXiv as a repository: Jumbled Titles and Mixed Titles. Our evaluation includes fifteen widely used language models and provides comparative insights into their reliability in handling scientific literature.

Cong-Thanh Do, Rama Doddipatla, Kate Knill

Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Distillation (KD) to transfer reasoning capability from a larger LLM to a smaller one. This paper examines the role of CoT in distilling the reasoning capability from larger LLMs to smaller LLMs using white-box KD, analysing its effectiveness in improving the performance of the distilled models for various natural language reasoning and understanding tasks. We conduct white-box KD experiments using LLMs from the Qwen and Llama2 families, employing CoT data from the CoT-Collection dataset. The distilled models are then evaluated on natural language reasoning and understanding tasks from the BIG-Bench-Hard (BBH) benchmark, which presents complex challenges for smaller LLMs. Experimental results demonstrate the role of CoT in improving white-box KD effectiveness, enabling the distilled models to achieve better average performance in natural language reasoning and understanding tasks from BBH.

Riccioni, Stefano, Penoni, Francesca, Spampinato, Beatrice

This article presents the project Observing South Caucasus’ Historical Landscape: An Open Photo Archive, whose strategic objectives are to collect, digitise, and catalogue a collection of photographs documenting the cultural heritage of the South Caucasus. The preservation and valorisation actions are targeted at three different facets of this heritage: (1) the tangible photographic collection (i.e. the photographic object itself); (2) the intangible historical layers (evolution of site ownership, historical stratification, and corresponding toponymy); (3) the tangible architectural and natural heritage of historical Armenia and Georgia.