Austria

This paper introduces PhoAudiobook, a newly curated dataset comprising 941 hours of high-quality audio for Vietnamese text-to-speech. Using PhoAudiobook, we conduct experiments on three leading zero-shot TTS models: VALL-E, VoiceCraft, and XTTS-V2. Our findings demonstrate that PhoAudiobook consistently enhances model performance across various metrics. Moreover, VALL-E and VoiceCraft exhibit superior performance in synthesizing short sentences, highlighting their robustness in handling diverse linguistic contexts. We publicly release PhoAudiobook to facilitate further research and development in Vietnamese text-to-speech.

ACL 2025

Zero-Shot Text-to-Speech for Vietnamese

vietnamese

text-to-speech

zero-shot

dataset

## 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 The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or "false"---as is frequently the case with scientific and political claims. However, a claim (e.g., "vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., "how many biomedical papers believe vaccine A is more transportable than B?"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.

Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims

Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce *FLARE*, short for facilitating LLM for advanced reasoning on wildfire evacuation decision prediction, a Large Language Model (LLM)-based framework that integrates behavioral theories and models to streamline the Chain-of-Thought (CoT) reasoning and subsequently integrate with memory-based Reinforcement Learning (RL) module to provide accurate evacuation decision prediction and understanding. Our proposed method addresses the limitations of using existing LLMs for evacuation behavioral predictions, such as limited survey data, mismatching with behavioral theory, conflicting individual preferences, implicit and complex mental states, and intractable mental state-behavior mapping. Experiments on three post-wildfire survey datasets show an average of 20.47\% performance improvement over traditional theory-informed behavioral models, with strong cross-event generalizability. Our complete code is publicly available at https://github.com/SusuXu-s-Lab/FLARE

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs

During a conversation, there can come certain moments where its outcome hangs in the balance. In these pivotal moments, how one responds can put the conversation on substantially different trajectories leading to significantly different outcomes. Systems that can detect when such moments arise could assist conversationalists in domains with highly consequential outcomes, such as mental health crisis counseling.

In this work, we introduce an unsupervised computational method for detecting such pivotal moments as they happen. The intuition is that a moment is pivotal if our expectation of the conversation’s outcome varies widely depending on what might be said next. By applying our method to crisis counseling conversations, we first validate it by showing that it aligns with human perception—counselors take significantly longer to respond during moments detected by our method—and with the eventual conversational trajectory—which is more likely to change course at these times. We then use our framework to explore the relation between the counselor’s response during pivotal moments and the eventual outcome of the session.

Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations

The demand for synthetic data in mathematical reasoning has increased due to its potential to enhance the mathematical capabilities of large language models (LLMs). However, ensuring the validity of intermediate reasoning steps remains a significant challenge, affecting data quality. While formal verification via theorem provers effectively validates LLM reasoning, the autoformalisation of mathematical proofs remains error-prone. In response, we introduce *iterative autoformalisation*, an approach that iteratively refines theorem prover formalisation to mitigate errors, thereby increasing the execution rate on the Lean prover from 60% to 87%. Building upon that, we introduce *Theorem Prover as a Judge (TP-as-a-Judge)*, a method that employs theorem prover formalisation to rigorously assess LLM intermediate reasoning, effectively integrating autoformalisation with synthetic data generation. Finally, we present *Reinforcement Learning from Theorem Prover Feedback (RLTPF),* a framework that replaces human annotation with theorem prover feedback in Reinforcement Learning from Human Feedback (RLHF). Across multiple LLMs, applying *TP-as-a-Judge* and *RLTPF* improves benchmarks with only 3,508 samples, achieving 5.56% accuracy gain on Mistral-7B for MultiArith, 6.00% on Llama-2-7B for SVAMP, and 3.55% on Llama-3.1-8B for AQUA.

Theorem Prover as a Judge for Synthetic Data Generation

Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link generated content to document-level references, making it challenging for users to locate evidence among multiple content-rich retrieved documents. To address this challenge, we propose Retrieval-Augmented Generation with Visual Source Attribution (VISA), a novel approach that combines answer generation with visual source attribution. Leveraging large vision-language models (VLMs), VISA identifies the evidence and highlights the exact regions that support the generated answers with bounding boxes in the retrieved document screenshots. To evaluate its effectiveness, we curated two datasets: Wiki-VISA, based on crawled Wikipedia webpage screenshots, and Paper-VISA, derived from PubLayNet and tailored to the medical domain. Experimental results demonstrate the effectiveness of VISA for visual source attribution on documents' original look, as well as highlighting the challenges for improvement.

VISA: Retrieval Augmented Generation with Visual Source Attribution

Transformer-based models have achieved state-of-the-art performance in document classification but struggle with long-text processing due to the quadratic computational complexity in the self-attention module. Existing solutions, such as sparse attention, hierarchical models, and key sentence extraction, partially address the issue but still fall short when the input sequence is exceptionally lengthy. To address this challenge, we propose **IRIS** (**I**nterpretable **R**etrieval-Augmented Classification for long **I**nterspersed Document **S**equences), a novel, lightweight framework that utilizes retrieval to efficiently classify long documents while enhancing interpretability. IRIS segments documents into chunks, stores their embeddings in a vector database, and retrieves those most relevant to a given task using learnable query vectors. A linear attention mechanism then aggregates the retrieved embeddings for classification, allowing the model to process arbitrarily long documents without increasing computational cost and remaining trainable on a single GPU. Our experiments across six datasets show that IRIS achieves comparable performance to baseline models on standard benchmarks, and excels in three clinical note disease risk prediction tasks where documents are extremely long and key information is sparse. Furthermore, IRIS provides global interpretability by revealing a clear summary of key risk factors identified by the model. These findings highlight the potential of IRIS as an efficient and interpretable solution for long-document classification, particularly in healthcare applications where both performance and explainability are crucial.

IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences

Accurately predicting human scanpaths during
reading is vital for diverse fields and downstream tasks, from educational technologies to
automatic question answering. To date, however, progress in this direction remains limited by scarce gaze data. We overcome the
issue with ScanEZ, a self-supervised framework grounded in cognitive models of reading.
ScanEZ jointly models the spatial and temporal dimensions of scanpaths by leveraging synthetic data and a 3-D gaze objective inspired by
masked language modeling. With this framework, we provide evidence that two key factors
in scanpath prediction during reading are: the
use of masked modeling of both spatial and
temporal patterns of eye movements, and cognitive model simulations as an inductive bias
to kick-start training. Our approach achieves
state-of-the-art results on established datasets
(e.g., up to 31.4% negative log-likelihood improvement on CELER L1), and proves portable
across different experimental conditions.

ScanEZ: Integrating Cognitive Models with Self-Supervised Learning for Spatiotemporal Scanpath Prediction

In spoken communication, information is transmitted not only via words, but also through a rich array of non-verbal signals, including prosody---the non-segmental auditory features of speech. Do these different communication channels carry distinct information? Prior work has shown that the information carried by prosodic features is substantially redundant with that carried by the surrounding words. Here, we systematically examine the time scale of this relationship, studying how it varies with the length of past and future contexts. We find that a word's prosodic features require an extended past context (3-8 words across different features) to be reliably predicted. Given that long-scale contextual information decays in memory, prosody may facilitate communication by adding information that is locally unique. We also find that a word’s prosodic features show some redundancy with future words, but only with a short scale of 1-2 words, consistent with reports of incremental short-term planning in language production. Thus, prosody may facilitate communication by helping listeners predict upcoming material. In tandem, our results highlight potentially distinct roles that prosody plays in facilitating integration of words into past contexts and in helping predict upcoming words.

The time scale of redundancy between prosody and linguistic context

Reading is a process that unfolds across space and time. Standard modeling approaches, however, overlook much of the spatio-temporal dynamics involved in reading by relying on aggregated reading measurements---typically only focusing on fixation durations---and employing models with strong simplifying assumptions. In this paper, we propose a generative model that captures not only how long fixations last, but also where they land and when they occur. To this end, we model reading scanpaths via two conditionally independent distributions: one for fixation location and timing, and another for fixation duration.
The location (and timing) of fixation shifts, so-called saccades, are modeled using a spatio-temporal Hawkes process, which captures how each fixation excites the probability of a new fixation occurring near it in time and space. Empirically, our Hawkes process model exhibits higher likelihood on held-out reading data than baselines. The duration time of fixation events is modeled as a function of fixation-specific features convolved across time, thus capturing non-stationary delayed effects. We find that convolution-based approaches demonstrate weak predictive power when modeling disaggregated fixation durations. Similarly, our analysis of surprisal theory on disaggregated data reveals limited effectiveness in predicting both where fixations occur and how long they last.

Fine-Grained Spatio-Temporal Modeling of Reading Behavior

Large language models (LLMs) excel at few-shot in-context learning (ICL) without requiring parameter updates. 
However, as ICL demonstrations increase from a few to many, performance tends to plateau and eventually decline. 
We identify two primary causes for this trend: the suboptimal negative log-likelihood (NLL) optimization objective and the incremental data noise. 
To address these issues, we introduce \textit{DrICL}, a novel optimization method that enhances model performance through \textit{Differentiated} and \textit{Reweighting} objectives. 
Globally, DrICL utilizes differentiated learning to optimize the NLL objective, ensuring that many-shot performance surpasses zero-shot levels. 
Locally, it dynamically adjusts the weighting of many-shot demonstrations by leveraging cumulative advantages inspired by reinforcement learning, thereby mitigating the impact of noisy data.
Recognizing the lack of multi-task datasets with diverse many-shot distributions, we develop the \textit{Many-Shot ICL Benchmark} (ICL-50)-a large-scale benchmark of 50 tasks that cover shot numbers from 1 to 350 within sequences of up to 8,000 tokens-for both fine-tuning and evaluation purposes.
Experimental results demonstrate that LLMs enhanced with DrICL achieve significant improvements in many-shot setups across various tasks, including both in-domain and out-of-domain scenarios.
We release the code and dataset hoping to facilitate further research in many-shot ICL\footnote{https://github.com/xiaoqzhwhu/DrICL}.

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Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims

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