China

Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate and speed up the data collection and annotation process, we introduce the task of detecting WMN indicators – utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data.

EMNLP 2025

Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation

conversational alignment

word meaning

misunderstanding

**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!
"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*

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

Stance detection, a critical task in Natural Language Processing (NLP), aims to identify the attitude expressed in text toward specific targets. Despite advancements in Large Language Models (LLMs), challenges such as limited interpretability and handling nuanced content persist. To address these issues, we propose the Multi-Path Reasoning Framework (MPRF), a novel framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection. Unlike prior work that relies on single-path reasoning or static explanations, MPRF introduces a structured end-to-end pipeline: it first generates diverse reasoning paths through predefined perspectives, then dynamically evaluates and optimizes each path using LLM-based scoring, and finally fuses the results via weighted aggregation to produce interpretable and reliable predictions. Extensive experiments on the SEM16, VAST, and PStance datasets demonstrate that MPRF outperforms existing models. Ablation studies further validate the critical role of MPRF’s components, highlighting its effectiveness in enhancing interpretability and handling complex stance detection tasks.

MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework

Multimodal Stance Detection (MSD) aims to determine a user’s stance—support, oppose, or neutral—toward a target by analyzing multimodal content such as text and images from social media. Existing MSD methods struggle with generalizing to unseen targets and handling modality inconsistencies. To address these challenges, we propose the Target-driven Multi-modal Alignment and Dynamic Weighting Model (T-MAD), which combines target-driven multi-modal alignment and dynamic weighting mechanisms to capture target-specific relationships and balance modality contributions. The model incorporates iterative reasoning to iteratively refine predictions, achieving robust performance in both in-target and zero-shot settings. Experiments on the MMSD and MultiClimate datasets show that T-MAD outperforms state-of-the-art models, with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. Ablation studies further confirm the importance of multi-modal alignment and dynamic weighting in enhancing model effectiveness.

T-MAD: Target-driven Multimodal Alignment for Stance Detection

While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist (e.g., hallucination), especially in the medical domain. Identifying citations from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context. First, we annotate 500 medical abstracts for seven key medical aspects, yielding 3.5K summary-citations pairs. We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics. Finally, we introduce a summarization pipeline, Track-Then-Sum, which serves as a baseline method for comparison. In experiments, we evaluate both this baseline and a set of LLMs on TracSum, and conduct a human evaluation to assess the evaluation results. The findings demonstrate that TracSum can serve as an effective benchmark for traceable, aspect-based summarization tasks. We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves summary completeness. The visualized dataset is anonymously available at https://www.tracsum.info/.

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain

Video-to-text summarization remains underexplored in terms of comprehensive evaluation methods. Traditional n-gram overlap-based metrics and recent large language model (LLM)-based approaches depend heavily on human-written reference summaries, limiting their practicality and sensitivity to nuanced semantic aspects. In this paper, we propose QEVA, a reference-free metric evaluating candidate summaries directly against source videos through multimodal question answering. QEVA assesses summaries along three clear dimensions: Coverage, Factuality, and Temporal Coherence. We also introduce MLVU(VS)-Eval, a new annotated benchmark derived from the MLVU dataset, comprising 800 summaries generated from 200 videos using state-of-the-art video-language multimodal models. This dataset establishes a transparent and consistent framework for evaluation. Experimental results demonstrate that QEVA shows higher correlation with human judgments compared to existing approaches, as measured by Kendall’s τb, τc, and Spearman’s ρ. We hope that our benchmark and metric will facilitate meaningful progress in video-to-text summarization research and provide valuable insights for the development of future evaluation methods.

QEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering

Vision-language models (VLMs) have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks. However, it is not clear how these models reason over the visual and textual data together, nor how the flow of information between modalities is structured. In this paper, we examine how VLMs reason by analyzing their biases when confronted with scenarios that present conflicting image and text cues—a common occurrence in real-world applications. To uncover the extent and nature of these biases, we build upon existing benchmarks to create five datasets containing mismatched image-text pairs, covering topics in mathematics, science, and visual descriptions. Our analysis shows that VLMs favor text in simpler queries but shift toward images as query complexity increases. This bias correlates with model scale, with the difference between the percentage of image- and text-preferred responses ranging from +56.8% (image favored) to -74.4% (text favored), depending on the task and model. In addition, we explore three mitigation strategies: simple prompt modifications, modifications that explicitly instruct models on how to handle conflicting information (akin to chain-of-thought prompting), and a task decomposition strategy that analyzes each modality separately before combining their results. Our findings indicate that the effectiveness of these strategies in identifying and mitigating bias varies significantly and is closely linked to the model's overall performance on the task and the specific modality in question. We will release our dataset and code.

Mixed Signals: Decoding VLMs' Reasoning and Underlying Bias in Vision-Language Conflict

Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. While POS tagging for MSA has advanced thanks to the availability of annotated datasets, creating similar resources for dialects remains costly and labor-intensive. Increasing the size of annotated datasets does not necessarily result in better performance. Active learning offers a more efficient alternative by prioritizing annotating the most informative samples. This paper proposes an active learning approach for multidialectal Arabic POS tagging. Our experiments show that annotating approximately 15,000 tokens is sufficient for high performance. We further demonstrate that using a fine-tuned model from one dialect to guide the selection of initial samples from another dialect accelerates convergence—reducing the annotation requirement by about 2,000 tokens. Finally, we present an active learning pipeline and show that, at its stopping point with 16,000 annotated tokens, our pipeline achieves 97.6\% accuracy on the Emirati Corpus.

Active Learning for Multidialectal Arabic POS Tagging

We introduce GreekBarBench, a benchmark that evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams, requiring citations to statutory articles and case facts. To tackle the challenges of free-text evaluation, we propose a three-dimensional scoring system combined with an LLM-as-a-judge approach. We also develop a meta-evaluation benchmark to assess the correlation between LLM-judges and human expert evaluations, revealing that simple, span-based rubrics improve their alignment. Our systematic evaluation of 13 proprietary and open-weight LLMs shows that even though the best models outperform average expert scores, they fall short of the 95th percentile of experts.

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations

Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis. To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge. In this survey, we provide a comprehensive overview of these methods, which we categorize into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. Each approach offers unique mechanisms to equip LLMs with domain expertise, balancing trade-offs between flexibility, scalability, and efficiency. We discuss how these methods enable LLMs to tackle specialized tasks, compare their advantages and disadvantages, evaluate domain-specific LLMs against general LLMs, and highlight the challenges and opportunities in this emerging field. For those interested in delving deeper into this area, we also summarize the commonly used datasets and benchmarks. To keep researchers updated on the latest studies, we maintain an open-source at: \faGithub ~\href{https://github.com/abilliyb/Knowledge_Injection_Survey_Papers}{\textcolor{blue}{official-repo.com}}, dedicated to documenting research in the field of specialized LLM.

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey

Reliable multi-image geological reasoning is essential for automating expert tasks in remote-sensing mineral exploration, yet remains challenging for multimodal large language models (MLLMs) due to the need for locating target areas, accurate cross-image referencing, and consistency over long reasoning chains. We propose STA-CoT, a Structured Target-centric Agentic Chain-of-Thought framework that orchestrates planning, execution, and verification agents to decompose, ground, and iteratively refine reasoning steps over geological and hyperspectral image sets. By aligning each reasoning step to specific image target areas and enforcing consistency through agentic verification and majority voting, STA-CoT robustly mitigates tool errors, long-chain inconsistencies, and error propagation. We rigorously evaluate STA-CoT on MineBench, a dedicated benchmark for multi-image mineral exploration, demonstrating substantial improvements over existing multimodal chain-of-thought and agentic baselines. Our results establish STA-CoT as a reliable and robust solution for consistent multi-image geological reasoning, advancing automated scientific discovery in mineral exploration.

STA-CoT: Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning

We propose KAHAN, a knowledge-augmented hierarchical framework that systematically extracts insights from raw, tabular data at entity, pairwise, group, and system levels. Uniquely, KAHAN leverages LLMs as domain experts to drive the analyses. Evaluation on the DataTales financial reporting benchmark shows KAHAN outperforms existing approaches by over 15% with GPT-4o, significantly improving insight quality while maintaining factuality (98.2%). Our results reveal that knowledge quality drives model performance through distillation, while hierarchical analysis benefits vary with market complexity. The data and code is available at https://anonymous.4open.science/r/kahan-0317.

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