India

Learners of a second language (L2) often map non-native phonemes to similar native-language (L1) phonemes, making conventional L2-focused training slow and effortful. To address this, we propose an L1-grounded pronunciation training method based on compositional phoneme approximation (CPA), a feature-based representation technique that approximates L2 sounds with sequences of L1 phonemes.
Evaluations with 20 Korean non-native English speakers show that CPA-based training achieves a 76\% in-box formant rate in acoustic analysis, 17.6\% relative improvement in phoneme recognition accuracy, and over 80\% of speech being rated as more native-like, with minimal training. Project page: \url{https://gsanpark.github.io/CPA-Pronunciation}.

IJCNLP-AACL 2025

Compositional Phoneme Approximation for L1-Grounded L2 Pronunciation Training

cross-linguistic phoneme mapping

speech learning and perception

computer-assisted pronunciation training

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
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The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Multimodal sentiment analysis identifies human emotional tendencies by analyzing text, visual, and auditory modalities. In most studies, the textual modality is usually considered to contain the most emotional information and is regarded as the dominant modality. Existing methods mostly map auxiliary modalities into a semantic space close to the dominant modality, which overly relies on the dominant modality. In this work, we propose a Feature Decomposition-Augmentation (FeaDA) framework, which aims to elevate the role of auxiliary modalities in multimodal data fusion. We first design a projector to decompose auxiliary modalities into partial features, which contain features for emotion judgment, and then utilize these decomposed features to guide the fusion process with KL loss, thereby enhancing the status of auxiliary modality fusion. To verify the effectiveness of our method, we conducted experiments on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets. The experimental results show that our FeaDA framework outperforms mutilmodal sentiment analysis methods of the same type in main metrics. Our code is available at https://github.com/PowerLittleYin/FeaDA-main.

Feature Decomposition-Augmentation Network for Multimodal Sentiment Analysis

Large language models (LLMs) are widely used for modeling sentiment trends on social media text. We examine whether LLMs have a polarity association bias---positive or negative---when encountering specific types of lexical word mentions. Such polarity association bias could lead to the wrong classification of neutral statements and thus a distorted estimation of sentiment trends. We estimate the severity of the polarity association bias across five widely used LLMs, identifying lexical word mentions spanning a diverse range of linguistic and psychological categories that correlate with this bias. Our results show a moderate to strong degree of polarity association bias in these LLMs.

To Labor is Not to Suffer: Exploration of Polarity Association Bias in LLMs for Sentiment Analysis

Claims that large language models (LLMs) have complex reasoning ability have stirred broad interests, and controversies, of academics and non-academics alike. A popular basis for such claims comes from LLMs' ability to solve coding problems, which involves understanding the problem statement and providing code that solves the problem. Although such abilities are remarkable feats worth praising, we argue that they come from memorization rather than reasoning. We first show that LLMs' problem-solving ability degrades with increased recency of the problem, likely due to the reduced amount of training data for more recent problems, regardless of the problem difficulty labeled by human experts. Additionally, we show that an LLM often fails to solve the problem when presented with reworded but equivalent problem statements, further suggesting their limited reasoning ability.

Large Language Models Exhibit Limited Reasoning Ability on Coding Problems

The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represented in pretraining data from generalization to out-of-domain language, specifically the dynamic, real-world instances less common in pretraining data. To this end, we construct a diagnostic evaluation to systematically assess natural language understanding in LLMs by leveraging Construction Grammar (CxG). CxG provides a psycholinguistically grounded framework for testing generalization, as it explicitly links syntactic forms to abstract, non-lexical meanings. Our novel inference evaluation dataset consists of English phrasal constructions, for which speakers are known to be able to abstract over commonplace instantiations in order to understand and produce creative instantiations. Our evaluation dataset uses CxG to evaluate two central questions: first, if models can "understand" the semantics of sentences for instances that are likely to appear in pretraining data less often, but are intuitive and easy for people to understand. Second, if LLMs can deploy the appropriate constructional semantics given constructions that are syntactically identical but with divergent meanings. Our results demonstrate that state-of-the-art models, including GPT-o1, exhibit a performance drop of over 40% on our second task, revealing a failure to generalize over syntactically identical forms to arrive at distinct constructional meanings in the way humans do. We make our novel dataset and associated experimental data, including prompts and model responses, publicly available.

Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions

Large language models (LLMs) exhibit inherent limitations in working memory, which often affect their overall capabilities. However, prior studies have largely focused on describing such constraints without identifying their causes or providing practical strategies to cope with them. In this paper, we investigate the limited working memory capacity of LLMs through a series of empirical studies. Specifically, we examine the factors involved in the limited capacity and explore strategies to make more effective use of it. Our analysis shows that the number and difficulty of tasks in a single input largely strain the working memory of LLMs. In response, we design a cognitive marker consisting of simple token sequences theoretically grounded in cognitive science. Further analyses show that the cognitive marker reduces the overall prediction difficulty and uncertainty for the models to process the input, and its effectiveness is confirmed across various evaluation settings. Overall, our study incorporates cognitively motivated perspectives into the analysis of model behavior and highlights the need for deeper exploration of working memory in LLMs.

Exploring Working Memory Capacity in LLMs: From Stressors to Human-Inspired Strategies

Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by promoting hallucinations, weakening long-range and retrieval abilities, and reducing robustness. Recent work has shown that differential design can mitigate this issue in Transformers, improving their effectiveness across various applications. In this paper, we explore whether these techniques, originally developed for Transformers, can be applied to Mamba, a recent architecture based on selective state-space layers that achieves Transformer-level performance with greater efficiency. We show that a naive adaptation of differential design to Mamba is insufficient and requires careful architectural modifications. To address this, we introduce a novel differential mechanism for Mamba, empirically validated on language modeling benchmarks, demonstrating improved retrieval capabilities and superior performance over vanilla Mamba. Finally, we conduct extensive ablation studies and empirical analyses to justify our design choices and provide evidence that our approach effectively mitigates the overallocation problem in Mamba-based models.

Differential Mamba

We present FOCUS, a benchmark and task setting for Socratic question generation that delivers more informative and targeted feedback to learners. Unlike prior datasets, which rely on broad typologies and lack grounding in the source text, FOCUS introduces a new formulation: each Socratic question is paired with a fine-grained, 11-type typology and an explicit source span from the argument it targets. This design supports clearer, more actionable feedback and facilitates interpretable model evaluation. FOCUS includes 440 annotated instances with moderate partial-match agreement, establishing it as a reliable benchmark. Baseline experiments with representative state-of-the-art models reveal, through detailed error analysis, that even strong models struggle with span selection and context-sensitive categories. An extension study on the LogicClimate dataset further confirms the generalizability of the task and annotation framework. FOCUS sets a new standard for pedagogically grounded and informative Socratic question generation.

FOCUS: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding

Entity-level sentiment analysis is becoming increasingly important in the context of diverse financial texts, and large language models demonstrate significant potential under zero-shot settings. While it is well recognized that different languages embody distinct cognitive patterns, the use of multilingual capabilities in large language models to enable cross-lingual collaborative reasoning in the financial domain remains insufficiently studied. To address this, we propose a Cross-Lingual Collaboration (CLC) method: first, financial texts are aligned from one language to another based on semantic and syntactic structures, enabling the model to capture complementary linguistic features. Then, we integrate sentiment analysis results from both languages through redundancy removal and conflict resolution, enhancing the effectiveness of cross-lingual collaboration. Our experiments cover seven languages from three language families, including six UN official languages, and evaluate CLC on two English datasets and one Chinese dataset. Results show that multilingual collaboration improves sentiment analysis accuracy, especially among linguistically similar languages. Furthermore, stronger reasoning capabilities in LLMs amplify these benefits. Our code is available at https://anonymous.4open.science/r/Cross-lingual-Collaboration.

EFSA-CLC: Enhancing Zero-shot Entity-level Financial Sentiment Analysis with Cross-lingual Collaboration

Temporal question answering (TQA) remains a persistent challenge for large language models (LLMs), particularly in retrieval-augmented generation (RAG) settings where retrieved content may be irrelevant, outdated, or temporally inconsistent. This is especially critical in applications like clinical event ordering, policy tracking, and real-time decision-making, which require reliable temporal reasoning even under noisy or misleading context. To address this challenge, we introduce RASTeR: Robust, Agentic, and Structured, Temporal Reasoning, an agentic prompting framework that separates context evaluation from answer generation. RASTeR first assesses the relevance and temporal coherence of retrieved context, then constructs a structured temporal knowledge graph (TKG) to better facilitate reasoning. When inconsistencies are detected, RASTeR selectively corrects or discards context before generating an answer. Across multiple datasets and LLMs, RASTeR consistently improves robustness: defined here as the model's ability to generate correct predictions despite suboptimal context. We further validate our approach through a ``needle-in-the-haystack'' study, in which relevant context is buried among irrelevant distractors. Even with forty distractors, RASTeR achieves 75% accuracy, compared to the runner-up model, which reaches only 62%.

RASTeR: Robust, Agentic, and Structured Temporal Reasoning

Pretraining on massive corpora has given rise to large language models (LLMs) with multi-task capabilities. However, real-world applications often require more specialized training, as is the case of NL2Code. We approach this specialization through the lens of data selection, i.e., identifying a subset of a large corpus that aligns with a desired target distribution---a challenge that remains under-explored within NL2Code. Existing methods are typically designed for selecting instruction-tuning data, and might not easily scale to large-scale code repositories; while methods for NL2Code do exist, they primarily rely on coarse heuristics—--such as repo stars—--for filtering. To bridge this gap, we propose FINDR, an efficient data selection method that extends logistic regression with feature-wise importance reweighting---marking it, to our knowledge, the first fine-grained solution to NL2Code pretraining. Our method uses hashed n-grams and code-aware features to capture code-specific patterns, and then apply informative priors to reweight feature importance when computing influence scores. Extensive experiments on NL2Python and NL2SQL, with two model families, show that FINDR consistently outperforms strong baselines in both execution accuracy and token efficiency. Notably, pretraining on only 2% of FINDR-selected data boosts Gemma by over 29% in both domains, even surpassing CodeGemma (pretrained on 300x more examples) by 10% in Python.

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Feature Decomposition-Augmentation Network for Multimodal Sentiment Analysis

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