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- NewsLanguage Magazine17 Jul 2026
Bilingualism Delays Brain Aging By 6 Years
New research presented at the FENS Forum 2026 shows that speaking multiple languages is linked to younger brain age, with bilingualism delaying brain aging by up to six years. Learning languages at a younger age and achieving fluency are particularly beneficial for slowing cognitive decline.
Original abstract
New research suggests that the more languages people speak, the younger their brains appear. Learning more languages, especially when younger, and achieving fluency in a second language also seem to slow brain aging. The research was presented at the recent Federation of European Neuroscience Societies (FENS) Forum 2026 by Dr Lucia Amoruso from the Basque […] The post Bilingualism Delays Brain Aging By 6 Years appeared first on Language Magazine .
- PaperTESOL Quarterly10 Jul 2026
The Grit–Goal Relationship in Language Learning as a Dynamic Process: A Goal Self‐Concordance Perspective
Hitoshi Mikami
A mixed-method study applies the goal self-concordance framework to examine how language learning goals aligned with one's sense of self are pursued, adjusted, or abandoned, and how L2 grit relates to these processes. Findings indicate that L2 grit levels do not always correspond to attainment of self-concordant goals, and that considering multiple goal layers is crucial for understanding the grit-goal relationship.
Original abstract
Second language (L2) grit, a noncognitive ability that underlies the pursuit of language goals, has attracted growing scholarly attention. However, the grit–goal relationship—including why certain goals are set and how grittiness relates to their pursuit—remains underexplored, as L2 grit has largely been examined in relation to researcher‐selected proxies of goals. In this context, this mixed method study applied the goal self‐concordance framework (Henry, 2021) to analyze how goals aligned with one's sense of self and future direction are pursued, adjusted, and sometimes abandoned and how L2 grit relates to these processes. Questionnaire and interview data were collected from fourth‐year English majors in Japan ( N = 8) to examine how L2 grit related to their goal development histories. The findings suggest that (a) L2 grit levels do not always correspond to the attainment of self‐concordant goals and (b) attention to multiple layers of goals may be important for understanding which aspects of L2 grit are associated with the pursuit of self‐concordant goals.
- PaperLanguage Teaching22 Jun 2026
A critical review of foreign language anxiety-reduction interventions: Toward a conceptual turn in foreign language anxiety research
Han Luo
This critical review synthesizes findings from experimental studies on foreign language anxiety-reduction interventions, categorizing them into emotion regulation, cognitive restructuring, skill building, and classroom environment enhancement. It argues for a conceptual shift from viewing anxiety as a symptom to be eliminated toward understanding it as a meaningful psychological signal, proposing an interdisciplinary approach integrating pedagogy with psychological insight.
Original abstract
Foreign language anxiety (FLA) has been widely studied over the past four decades, yet the effectiveness of anxiety-reduction strategies remains insufficiently understood. This article offers a critical review of research on FLA-reduction interventions, synthesizing the findings from experimental studies across four major categories: emotion regulation, cognitive restructuring, skill building, and classroom environment enhancement. While many strategies show promise, the field lacks a coherent, psychologically grounded framework for supporting anxious learners. Drawing on psychological theories including cognitive-behavioral, acceptance-based, and analytical frameworks and emerging Daoism-inspired psychological perspectives, this article argues for a conceptual shift in FLA research: from viewing anxiety as a surface-level symptom to be eliminated toward understanding it as a meaningful psychological signal that may reflect deeper learner-internal processes. The article proposes an interdisciplinary approach that integrates pedagogical practice with psychological insight, positioning anxiety as a potential catalyst for self-awareness and growth. Implications for language teaching and future directions for FLA research are discussed.
- PaperarXiv — Language & NLP (cs.CL)4 Jun 2026
Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
Hanxu Hu, Zdeněk Šnajdr, Pinzhen Chen, Jannis Vamvas et al.
Reinforcement learning with a surface-level translation metric (chrF) as reward enables LLMs to acquire the meta-skill of using in-context linguistic knowledge for translating completely unseen languages, outperforming both in-context learning and supervised fine-tuning. This approach goes beyond memorization and suggests a general recipe for language learning from context.
Original abstract
Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To translate extremely low-resource languages at scale, we argue that LLMs must acquire the meta-skill of utilizing in-context linguistic knowledge rather than memorizing specific languages. In this paper, we propose a reinforcement learning (RL) approach to unseen language translation given rich linguistic context, using a surface-level translation metric (chrF) as the reward. Empirically, despite the lightweight reward, our RL-trained models effectively extract and apply relevant linguistic information from the provided context, leading to better translations on completely unseen languages than in-context learning or supervised fine-tuning. Our analyses suggest that outcome-based RL can extend beyond conventional reasoning tasks like math and coding to serve as a recipe for language learning from context.
- PaperarXiv — Language & NLP (cs.CL)18 May 2026
Bridging the Gap: Converting Read Text to Conversational Dialogue
Parshav Singla, Agnik Banerjee, Aaditya Arora, Shruti Aggarwal et al.
Introduces Prosodic Adjustment with Conversational Context (PACC), a deep neural network approach using HiFi-GAN to convert read speech into natural conversational speech by modifying prosodic features like intonation, stress, and rhythm. Experimental results show significant improvements in naturalness and model accuracy, setting new benchmarks in speech conversion tasks with potential applications in language learning tools and virtual assistants.
Original abstract
In recent advancements within speech processing, converting read speech to conversational speech has gained significant attention. The primary challenge in this domain is maintaining naturalness and intelligibility while minimizing computational overhead for real-time applications. Traditional read speech often lacks the nuanced prosodic variation essential for natural conversational interactions, posing challenges for applications in virtual assistants, customer service, and language learning tools. This paper introduces a novel approach, Prosodic Adjustment with Conversational Context (PACC), aimed at converting read speech into natural conversational speech used in various modern applications. PACC utilizes advanced deep neural networks to analyze and modify prosodic features such as intonation, stress, and rhythm. Unlike conventional methods, our approach uses High-Fidelity Generative Adversarial Networks (HiFi-GAN) for speech synthesis. Our experimental results demonstrate significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy with additional training on speech datasets. This research establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation for testing model accuracy, and we show that our approach can be successfully extended to other speech conversion applications.
- PaperarXiv — Language & NLP (cs.CL)9 May 2026
Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling
Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tsamarah Rana Nugraha, Ahmad Cahyono Adi et al.
Proposes Context-Aligned Contrastive Regression, combining Ridge regression ensemble with cross-view context and ordinal soft contrastive learning to improve lexical difficulty prediction across different L1 backgrounds. Experiments show improved cross-lingual alignment, ordinal structure capture, and reduced model bias, leading to more stable performance.
Original abstract
Lexical difficulty prediction is a fundamental problem in language learning and readability assessment, requiring models to estimate word difficulty across different first-language (L1) backgrounds. However, existing approaches rely on regression-only training with scalar supervision, which does not explicitly structure the representation space, limiting their ability to capture cross-lingual alignment and ordinal difficulty. To mitigate these issues, we propose Context-Aligned Contrastive Regression, which integrates Ridge regression ensemble with two complementary objectives, i.e., Cross-View Context and Ordinal Soft Contrastive Learning. Experiments on three L1 datasets show that (i) contrastive objectives improve cross-lingual representation alignment while preserving language-specific nuances, (ii) the learned representations capture the ordinal structure of lexical difficulty, and (iii) the ensemble effectively mitigates systematic biases of individual models, leading to more stable performance across difficulty levels.