India

Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interventions are often infeasible, necessitating reliance on observational data. Traditional causal inference methods, typically designed for binary or discrete treatments, are inadequate for handling the complex, high-dimensional textual data. This paper addresses these challenges by proposing CausalDANN, a novel approach to estimate causal effects using text transformations facilitated by large language models (LLMs). Unlike existing methods, our approach accommodates arbitrary textual interventions and leverages text-level classifiers with domain adaptation ability to produce robust effect estimates against domain shifts, even when only the control group is observed. This flexibility in handling various text interventions is a key advancement in causal estimation for textual data, offering opportunities to better understand human behaviors and develop effective interventions within social systems.

IJCNLP-AACL 2025

Estimating Causal Effects of Text Interventions Leveraging LLMs

counterfactual generation

causal inference

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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.

The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Language Models (LLMs) for classifying the political ideology of online content through in-context learning (ICL). Our extensive experiments involving demonstration selection in label-balanced fashion, conducted on three datasets comprising news articles and YouTube videos, reveal that our approach significantly outperforms zero-shot and traditional supervised methods. Additionally, we evaluate the influence of metadata (e.g., content source and descriptions) on ideological classification and discuss its implications. Finally, we show how providing the source for political and non-political content influences the LLM's classification.

"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection

Emotion recognition methods typically assign labels at the sentence level, obscuring the specific linguistic cues that signal emotion. This limits their utility in applications requiring targeted responses, such as empathetic dialogue and clinical support, which depend on knowing which language expresses emotion. The task of identifying \textit{emotion evidence} -- text spans conveying emotion -- remains underexplored due to a lack of labeled data. Without span-level annotations, we cannot evaluate whether models truly localize emotion expression, nor can we diagnose the sources of emotion misclassification. We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to evaluate Large Language Models (LLMs) on this task. SEER evaluates single and multi-sentence span identification with new annotations on 1200 real-world sentences. We evaluate 14 LLMs and find that, on single-sentence inputs, the strongest models match the performance of average human annotators, but performance declines in multi-sentence contexts. Key failure modes include fixation on emotion keywords and false positives in neutral text.

SEER: The Span-based Emotion Evidence Retrieval Benchmark

Shaping inclusive representations that embrace diversity and ensure fair participation and reflections of values is at the core of many conversation-based models. However, many existing methods rely on surface inclusion using mention of user demographics or behavioral attributes of social groups. Such methods overlook the nuanced, implicit expression of opinion embedded in conversations. Furthermore, the over-reliance on overt cues can exacerbate misalignment and reinforce harmful or stereotypical representations in model outputs. Thus, we took a step back and recognized that equitable inclusion needs to account for the implicit expression of opinion and use the stance of responses to validate the normative alignment. This study aims to evaluate how opinions are represented in NLP or computational models by introducing an alignment evaluation framework that foregrounds implicit, often overlooked conversations and evaluates the normative social views and discourse. Our approach models the stance of responses as a proxy for the underlying opinion, enabling a considerate and reflective representation of diverse social viewpoints. We evaluate the framework using both (i) positive-unlabeled (PU) online learning with base classifiers, and (ii) instruction-tuned language models to assess post-training alignment. Through this, we provide a basis for understanding how implicit opinions are (mis)represented and offer a pathway toward more inclusive model behavior.

EMBRACE: Shaping Inclusive Opinion Representation by Aligning Implicit Conversations with Social Norms

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.

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

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.

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Next from IJCNLP-AACL 2025

"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection

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