India

Social norms are implicit, culturally grounded expectations that guide interpersonal communication. Unlike factual commonsense, norm reasoning is subjective, context-dependent, and varies across cultures—posing challenges for computational models. Prior works provide valuable normative annotations but mostly target isolated utterances or synthetic dialogues, limiting their ability to capture the fluid, multi-turn nature of real-world conversations. In this work, we present Norm-RAG, a retrieval-augmented, agentic framework for nuanced social norm inference in multi-turn dialogues. Norm-RAG models utterance-level attributes including communicative intent, speaker roles, interpersonal framing, and linguistic cues and grounds them in structured normative documentation retrieved via a novel Semantic Chunking approach. This enables interpretable and context-aware reasoning about norm adherence and violation across multilingual dialogues. We further introduce MINDS (Multilingual Interactions with Norm-Driven Speech), a bilingual dataset comprising 31 multi-turn Mandarin-English and Spanish-English conversations. Each turn is annotated for norm category and adherence status using multi-annotator consensus, reflecting cross-cultural and realistic norm expression. Our experiments show that Norm-RAG improves norm detection and generalization, demonstrates improved performance for culturally adaptive and socially intelligent dialogue systems.

IJCNLP-AACL 2025

MINDS: A Cross-Cultural Dialogue Corpus for Social Norm Classification and Adherence Detection

llm eval

semantic chunking

social norm classification in cross cultures

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

poster

### 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* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Despite significant progress in large language models (LLMs), their knowledge and evaluation continue to be centered around high-resource languages, leaving critical gaps in low-resource settings. This raises questions about how effectively LLMs handle subjects that require locally relevant knowledge. To address this challenge, we need a robust dataset that reflects the knowledge of underrepresented regions such as Bangladesh. In this paper, we present ***SOMAJGYAAN***, a Bangla multiple-choice dataset consisting of 4,234 questions, annotated across five levels of difficulty. The questions are drawn from Bangladesh’s National Curriculum and Global Studies textbooks, covering a wide range of domains including History, Geography, Economics, Social Studies, Politics and Law, and Miscellaneous topics. Difficulty levels were assigned by four expert annotators to minimize annotation bias. The experiments reveal that closed-source LLMs perform better than open-source LLMs. While fine-tuning open-source models on \name improves their performance, they still fall short of matching closed-source LLMs. Our findings highlight the importance of culturally grounded evaluation datasets and task-specific adaptation to improve LLM performance in low-resource language settings.

SOMAJGYAAN: A Dataset for Evaluating LLMs on Bangla Culture, Social Knowledge, and Low-Resource Language Adaptation

Recent studies have shown that prompting large language models (LLMs) with role-playing personas can enhance their reasoning capabilities. While the benefits of role-playing personas in reasoning tasks are widely recognized, it remains uncertain whether a persona aligned with the given dataset can consistently achieve these improvements. In this work, we empirically investigate the potential drawbacks of using dataset-aligned personas (referred to as **coarsely aligned personas**) and introduce Jekyll \& Hyde, a novel framework that enhances reasoning robustness by ensembling solutions from both role-playing and neutral (non-persona) prompts.
Jekyll \& Hyde first predicts an instance-specific persona tailored to each query using an LLM, then generates answers with both persona and neutral prompts, and finally selects the superior output through an LLM-based evaluator.
Experimental results claim that across twelve widely used natural language reasoning datasets and three backbone large language models, Jekyll \& Hyde consistently outperforms single-perspective LLMs, achieving an average accuracy gain of **9.98\%** on GPT‑4.
We further demonstrate that using instance‑aligned personas yields more accurate and stable performance than using dataset-aligned personas.

Persona is a Double-Edged Sword: Rethinking the Impact of Role-play Prompts in Zero-shot Reasoning Tasks

Gaps arise between a language model's use of concepts and people's expectations. This gap is critical when LLMs generate text to help people communicate via Augmentative and Alternative Communication (AAC) tools. In this work, we introduce the evaluation task of Representation Alignment for measuring this gap via human judgment. In our study, we expand keywords and emotion representations into full sentences. We select four emotion representations: Words, Valence-Arousal-Dominance (VAD) dimensions expressed in both Lexical and Numeric forms, and Emojis.  In addition to Representation Alignment, we also measure people's judgments of the accuracy and realism of the generated sentences. While representations like VAD break emotions into easy-to-compute components, our findings show that people agree more with how LLMs generate when conditioned on English words (e.g., "angry") rather than VAD scales. This difference is especially visible when comparing Numeric VAD to words. Furthermore, we found that the perception of how much a generated sentence conveys an emotion is dependent on both the representation type and which emotion it is.

Evaluating Human-LLM Representation Alignment: A Case Study on Affective Sentence Generation for Augmentative and Alternative Communication

Logical Table-to-Text (LT2T) generation requires models to both verbalize tabular data and reason over it - performing comparisons, aggregations, and causal inference. While many generation tasks struggle with similar analytical demands, LT2T provides a structured perspective on reasoning capabilities in natural language generation. This survey uses LT2T as a lens to focus on reasoning in data-to-text tasks. By focusing narrowly on LT2T, we present a deep taxonomy of methods that inject, structure, or verify reasoning steps, allowing a level of technical granularity missing in broader surveys. We review representative models and evaluation metrics, and highlight how LT2T techniques transfer to general generation challenges involving logic, numeracy, and faithfulness. Our goal is to distill lessons from LT2T that apply more widely, while also guiding future research in table-based reasoning.

Logical Table-to-Text Generation: Challenges, Methods, and Reasoning

Esports is a competitive sport in which highly skilled players face off in fast-paced video games. Matches consist of intense, moment-by-moment plays that require exceptional technique and strategy. These moments often involve complex interactions, including team fights, positioning, or strategic decisions, which are difficult to interpret without expert explanation. In this study, we set up the task of generating commentary for a specific game moment from multimodal game data consisting of a gameplay screenshot and structured JSON data. Specifically, we construct the first large-scale tri-modal dataset for League of Legends, one of the most popular multiplayer strategy esports titles, and then design evaluation criteria for the task. Using this dataset, we evaluate various large vision language models in generating commentary for a specific moment. We will release the scripts to reconstruct our dataset.

Commentary Generation from Multimodal Game Data for Esports Moments in Multiplayer Strategy Games

In this work, we address the problem of fine-grained traceback of emotional and manipulation characteristics from synthetically manipu- lated speech. We hypothesize that combining semantic–prosodic cues captured by Speech Foundation Models (SFMs) with fine-grained
spectral dynamics from auditory representations can enable more precise tracing of both emotion and manipulation source. To validate this hypothesis, we introduce MiCuNet, a novel multitask framework for fine-grained tracing of emotional and manipulation attributes in synthetically generated speech. Our approach integrates SFM embeddings with spectrogram-based auditory features through a mixed-curvature projection mechanism that spans Hyperbolic, Euclidean, and Spherical spaces guided by a learnable temporal gating mechanism. Our proposed method adopts a multitask learning setup to simultaneously predict original emotions, manipulated emotions, and manipulation sources on the Emo-Fake dataset (EFD) across both English and Chinese subsets. MiCuNet yields consistent improvements, consistently surpassing conventional fusion strategies. To the best of our knowledge, this work presents the first study to explore a curvature-adaptive framework specifically tailored for multitask tracking in synthetic speech.

Towards Attribution of Generators and Emotional Manipulation in Cross-Lingual Synthetic Speech using Geometric Learning

Recent advances in AI-generated video have shown strong performance on text-to-video tasks, particularly for short clips depicting a single scene. However, current models struggle to generate longer videos with coherent scene transitions, primarily because they cannot infer when a transition is needed from the prompt. Most open-source models are trained on datasets consisting of single-scene video clips, which limits their capacity to learn and respond to prompts requiring multiple scenes. Developing scene transition awareness is essential for multi-scene generation, as it allows models to identify and segment videos into distinct clips by accurately detecting transitions. To address this, we introduce the Transition-Aware Video (TAV) dataset with multi-scene clips and captions that explicitly state scene segmentation and transition structure. Our focus is on how prompt semantics and dataset annotations about temporal context affect text-to-video generation. Post-training on TAV improves alignment between the scene count implied by prompt and the scene count produced by the model, while preserving visual quality.

Enhancing Scene Transition Awareness in Video Generation via Post-Training

Rapid information access is vital during wildfires, yet traditional data sources are slow and costly. Social media offers real-time updates, but extracting relevant insights remains a challenge. In this work, we focus on multimodal wildfire social media data, which, although existing in current datasets, is currently underrepresented in Canadian contexts. We present WildFireCan-MMD, a new multimodal dataset of X posts from recent Canadian wildfires, annotated across twelve key themes. We evaluate zero-shot vision-language models on this dataset and compare their results with those of custom-trained and baseline classifiers. We show that while baseline methods and zero-shot prompting offer quick deployment, custom-trained models outperform them when labelled data is available. Our best-performing custom model reaches 84.48±0.69% f-score, outperforming VLMs and baseline classifiers. We also demonstrate how this model can be used to uncover trends during wildfires, through the collection and analysis of a large unlabeled dataset. Our dataset facilitates future research in wildfire response, and our findings highlight the importance of tailored datasets and task-specific training. Importantly, such datasets should be localized, as disaster response requirements vary across regions and contexts.

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

To explore a more scalable path for adding multimodal capabilities to existing LLMs, this paper addresses a fundamental question: Can a unimodal LLM, relying solely on text, reason about its own informational needs and provide effective feedback to optimize a multimodal model? To answer this, we propose a method that enables a language agent to give feedback to a vision-language model (VLM) to adapt text generation to the agent's preferences. Our results from different experiments affirm this hypothesis, showing that LLM preference feedback significantly enhances VLM descriptions. Using our proposed method, we find that the VLM can supply multimodal scene descriptions to help the LLM better understand multimodal context, leading to improvements of maximum 13% in absolute accuracy compared to the baseline multimodal approach. Furthermore, a human study validated our AI-driven feedback, showing a 64.6% preference alignment rate between the LLM's choices and human judgments. Extensive experiments provide insights on how and why the method works and its limitations.

Can a Unimodal Language Agent Provide Preferences to Tune a Multimodal Vision-Language Model?

Social networks extensively feature memes, particularly cartoon images, as a prevalent form of communication often conveying complex sentiments or harmful content. Detecting such content, particularly when it involves Bengali and English text, remains a multimodal challenge. This paper introduces ***CMBan***, a novel and culturally relevant dataset of 2,641 annotated cartoon memes. It addresses meme classification based on their sentiment across five key categories: Humor, Sarcasm, Offensiveness, Motivational Content, and Overall Sentiment, incorporating both image and text features. Our curated dataset specifically aids in detecting nuanced offensive content and navigating complexities of pure Bengali, English, or code-mixed Bengali-English languages. Through rigorous experimentation involving over 12 multimodal models, including monolingual, multilingual, and proprietary architectures, and utilizing prompting methods like Chain-Of-Thought (CoT), findings suggest this cartoon-based, code-mixed meme content poses substantial understanding challenges. Experimental results demonstrate that closed models excel over open models. While the LoRA fine-tuning strategy equalizes performance across model architectures and improves classification of challenging aspects in multilingual meme contexts, this work advances meme classification by providing effective solution for detecting harmful content in multilingual meme contexts.

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Next from IJCNLP-AACL 2025

SOMAJGYAAN: A Dataset for Evaluating LLMs on Bangla Culture, Social Knowledge, and Low-Resource Language Adaptation

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