United States

Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et al., 2023) and SAFE (Wei et al., 2024), decompose an input text into “atomic claims” and verify each against a knowledge base like Wikipedia. These metrics are not suitable for most generation tasks because they assume that every claim is verifiable (i.e., can plausibly be proven true or false). We address this issue with VERISCORE,1 a metric for evaluating factuality in diverse long-form generation tasks that contain both verifiable and unverifiable content. VERISCORE can be effectively implemented with either closed or fine-tuned open-weight language models. Human evaluation confirms that VERISCORE’s extracted claims are more sensible than those from competing methods across eight different long-form tasks. We use VERISCORE to evaluate generations from 16 different models across multiple long-form tasks and find that while GPT-4o is the best-performing model overall, open-weight models such as Mixtral-8×22 are closing the gap. We show that an LM’s VERISCORE on one task (e.g., biography generation) does not necessarily correlate to its VERISCORE on a different task (e.g., long-form QA), highlighting the need for expanding factuality evaluation across tasks with varying fact density.

EMNLP 2024

VeriScore: Evaluating the factuality of verifiable claims in long-form text generation

tree-based planning

llm education

educational dialogue

## 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 EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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Please register!

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.

DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking

With the continuous growth of online communities, understanding their similarities and dissimilarities is more crucial than ever for enhancing digital interactions, maintaining healthy interactions, and improving content recommendation and moderation systems. In this work, we present two novel techniques: BOTS for finding similarity between online communities based on their opinion, and Emb-PSR for finding similarity in the content they post. To facilitate finding the similarity based on opinion, we model the opinions on online communities using upvotes and downvotes as an indicator for community approval. Our results demonstrate that BOTS and Emb-PSR outperform existing techniques at their individual tasks while also being flexible enough to allow for cross-platform comparison of online communities. We demonstrate this novel cross-platform capability by comparing GAB with various subreddits.

Enabling Cross-Platform Comparison of Online Communities Using Content and Opinion Similarity

Psychological evidence reveals the influence of personality traits on decision-making. For instance, agreeableness is generally associated with positive outcomes in negotiations, whereas neuroticism is often linked to less favorable outcomes. This paper introduces a simulation framework centered on large language model (LLM) agents endowed with synthesized personality traits. The agents negotiate within bargaining domains and possess customizable personalities and objectives. The experimental results show that the behavioral tendencies of LLM-based simulations can reproduce behavioral patterns observed in human negotiations. The contribution is twofold. First, we propose a simulation methodology that investigates the alignment between the linguistic and economic capabilities of LLM agents. Secondly, we offer empirical insights into the strategic impacts of Big Five personality traits on the outcomes of bilateral negotiations. We also provide an in-depth analysis based on simulated bargaining dialogues to reveal intriguing behaviors, including deceitful and compromising behaviors.

How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models

This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To facilitate cross-linguistic transfer, we generate synthetic Hindi-Marathi and Marathi-Hindi APE triplets. Additionally, we incorporate a Quality Estimation (QE)-APE multi-task learning framework. While the experimental results underline the complementary nature of APE and QE, we also observe that QE-APE multitask learning facilitates effective domain adaptation. Our experiments demonstrate that the multilingual APE models outperform their corresponding English-Hindi and English-Marathi single-pair models by $2.5$ and $2.39$ TER points, respectively, with further notable improvements over the multilingual APE model observed through multi-task learning ($+1.29$ and $+1.44$ TER points), data augmentation ($+0.53$ and $+0.45$ TER points) and domain adaptation ($+0.35$ and $+0.45$ TER points). We release the synthetic data, code, and models accrued during this study publicly for further research.

Together We Can: Mulitlingual Automatic Post-Editing for Low-Resource Languages

Mental disorders pose a global challenge, aggravated by the shortage of qualified mental health professionals. Mental disorder prediction from social media posts by current LLMs is challenging due to the complexities of sequential text data and the limited context length of language models. Current language model-based approaches split a single data instance into multiple chunks to compensate for limited context size. The predictive model is then applied to each chunk individually, and the most voted output is selected as the final prediction. This results in the loss of inter-post dependencies and important time variant information, leading to poor performance. We propose a novel framework which first compresses the large sequence of chronologically ordered social media posts into a series of numbers. We then use this time variant representation for mental disorder classification. We demonstrate the generalization capabilities of our framework by outperforming the current SOTA in three different mental conditions: depression, self-harm, and anorexia, by an absolute improvement of 5% in the F1 score. We also investigate the situation when current data instances fall within the context length of language models and present empirical results highlighting the importance of temporal properties of textual data. Furthermore, we utilize the proposed framework for a cross-domain study, exploring commonalities across disorders and the possibility of inter-domain data usage.

Mental Disorder Classification via Temporal Representation of Text

In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challenges. In particular, compound sentences can potentially contain multiple quadruplets, making the extraction task increasingly difficult as sentence complexity grows. To address this issue, we are focusing on simplifying sentence structures to facilitate the easier recognition of these elements and crafting a model that integrates seamlessly with various ABSA tasks. In this paper, we propose Aspect Term Oriented Sentence Splitter (ATOSS), which simplifies compound sentence into simpler and clearer forms, thereby clarifying their structure and intent. As a plug-and-play module, this approach retains the parameters of the ABSA model while making it easier to identify essential intent within input sentences. Extensive experimental results show that utilizing ATOSS outperforms existing methods in both ASQP and ACOS tasks, which are the primary tasks for extracting sentiment quadruplets

Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis

Counterarguments (CAs) are a valuable tool in education for enhancing critical thinking skills. Despite their effectiveness, the logical attack structure of counterarguments in relation to their corresponding opponent argument remains unexplored due to its complexity. Towards tackling this challenge, in this work, we introduce $\textbf{C}$ounter-$\textbf{A}$rgument $\textbf{L}$ogical $\textbf{S}$tructure $\textbf{A}$nalysis ($\textbf{CALSA}$), a new task. We first define 10 new CA logic patterns, each comprised of a unique template and slots. We then conduct an annotation study on top of 778 CAs using our patterns to create a new dataset. Our dataset achieves high annotator agreement (Krippendorff $\alpha$=0.50) and high coverage (86.5\%). We perform preliminary experiments employing recent large language models to assess the feasibility of automating CA logical structure analysis. Our findings highlight the task's inherent complexity within a straightforward framework, indicating exciting opportunities for further exploration. We publicly release our dataset and model scripts at https://github.com/cl-tohoku/CALSA.

Designing Logic Pattern Templates for Counter-Argument Logical Structure Analysis

The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a scholarship depends on eligibility conditions, such as major or degree required. ConditionalQA was proposed to evaluate models' capability of reading a document and answering eligibility questions, considering *unmentioned* conditions. However, it is limited to questions on single documents, neglecting harder cases that may require *cross-document reasoning* and *optimization*, for example, "What is the maximum number of scholarships attainable?" Such questions over multiple documents are not only more challenging due to more context to understand, but also because the model has to (1) explore all possible combinations of unmentioned conditions and (2) understand the relationship between conditions across documents, to reason about the optimal outcome. To evaluate models' capability of answering such questions, we propose a new dataset MDCR, which can reflect real-world challenges and serve as a new test bed for complex conditional reasoning that requires optimization. We evaluate this dataset using the most recent LLMs and demonstrate their limitations in solving this task. We believe this dataset will facilitate future research in answering optimization questions with unknown conditions.

MDCR: A Dataset for Multi-Document Conditional Reasoning

Employing Large Language Models (LLMs) to assess the quality of generated responses has become a widely adopted evaluation method. Specifically, instruct-tuned models and fine-tuned judge models based on open-source LLMs have been reported. While it is known that judge models are vulnerable to certain biases, such as favoring longer answers regardless of content, the specifics of these biases remain under-explored. In this work, we qualitatively identify six types of biases inherent in various judge models. We propose EvalBiasBench as a meta-evaluation collection of hand-crafted test cases for each bias type. Additionally, we present de-biasing dataset construction methods and the associated preference dataset OffsetBias. Experimental results demonstrate that fine-tuning on our dataset significantly enhances the robustness of judge models against biases and improves performance across most evaluation scenarios. We release our datasets and the fine-tuned judge model to public.

OffsetBias: Leveraging Debiased Data for Tuning Evaluators

As generative AI progresses, collaboration be-
tween doctors and AI scientists is leading to the
development of personalized models to stream-
line healthcare tasks and improve productivity.
Summarizing doctor-patient dialogues has be-
come important, helping doctors understand
conversations faster and improving patient care.
While previous research has mostly focused on
text data, incorporating visual cues from pa-
tient interactions allows doctors to gain deeper
insights into medical conditions. Most of this
research has centered on English datasets, but
real-world conversations often mix languages
for better communication. To address the lack
of resources for multimodal summarization of
code-mixed dialogues in healthcare, we devel-
oped the MCDH dataset. Additionally, we cre-
ated HealthAlignSumm, a new model that in-
tegrates visual components with the BART ar-
chitecture. This represents a key advancement
in multimodal fusion, applied within both the
encoder and decoder of the BART model. Our
work is the first to use alignment techniques,
including state-of-the-art algorithms like Direct
Preference Optimization, on encoder-decoder
models with synthetic datasets for multimodal
summarization. Through extensive experi-
ments, we demonstrated the superior perfor-
mance of HealthAlignSumm across several
metrics validated by both automated assess-
ments and human evaluations. The dataset
MCDH and our proposed model HealthAlign-
Summ will be available in this GitHub account
https://github.com/AkashGhosh/Health
AlignSumm-Utilizing-Alignment-for-M
ultimodal-Summarization-of-Code-Mix
ed-Healthcare-Dialogues
Disclaimer: This work involves medical im-
agery based on the subject matter of the topic.

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Next from EMNLP 2024

DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking

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VeriFastScore: Speeding up long-form factuality evaluation