Austria

ACL 2025

SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval

workshop paper

### 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)

Pre-trained language models have significantly advanced natural language processing (NLP), particularly in analyzing languages with complex morphological structures. This study addresses lemmatization for the Russian language, the errors in which can critically affect the performance of information retrieval, question answering, and other tasks. We present the results of experiments on generative lemmatization using pre-trained language models. Our findings demonstrate that combining generative models with the existing solutions allows achieving performance that surpasses current results for the lemmatization of Russian. This paper also introduces Rubic2, a new ensemble approach that combines the generative BART-base model, fine-tuned on a manually annotated data set of 2.1 million tokens, with the neural model called Rubic which is currently used for morphological annotation and lemmatization in the Russian National Corpus. Extensive experiments show that Rubic2 outperforms current solutions for the lemmatization of Russian, offering superior results across various text domains and contributing to advancements in NLP applications.

Rubic2: Ensemble Model for Russian Lemmatization

Large language models (LLMs) often reflect social biases present in their training data, including imbalances in how different genders are represented. While most prior work has focused on English, gender representation bias remains underexplored in morphologically rich languages where grammatical gender is pervasive. We present a method for detecting and quantifying such bias in Czech and Slovenian, using LLMs to classify gendered person references in LLM-generated narratives. Applying this method to outputs from a range of models, we find substantial variation in gender balance. While some models produce near-equal proportions of male and female references, others exhibit strong male overrepresentation. Our findings highlight the need for fine-grained bias evaluation in under-represented languages and demonstrate the potential of LLM-based annotation in this space. We make our code and data publicly available.

Gender Representation Bias Analysis in LLM-Generated Czech and Slovenian Texts

Proactive dialogue systems aim to empower chatbots with the capability of leading conversations towards specific targets, thereby enhancing user engagement and service autonomy. Existing systems typically target pre-defined keywords or entities, neglecting user attributes and preferences implicit in dialogue history, hindering the development of long-term user intimacy. To address these challenges, we take a radical step towards building a more human-like conversational agent by integrating proactive dialogue systems with long-term memory into a unified framework. Specifically, we define a novel task named Memory-aware Proactive Dialogue (MapDia). By decomposing the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive Dataset (ChMapData). Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation, designed to steer dialogues towards relevant historical topics at the right time. The effectiveness of our dataset and models is validated through both automatic and human evaluations.

Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History

Recent work has demonstrated that neural language models encode syntactic structures in their internal *representations*, yet the *derivations* by which these structures are built across layers remain poorly understood. 
In this paper, we introduce *Derivational Probing* to investigate how micro-syntactic structures (e.g., subject phrases) and macro-syntactic structures (e.g., the relationship between the root verb and its direct dependents) are progressively constructed as word embeddings propagate upward across layers.
Our experiments on BERT reveal a clear bottom-up derivation: micro-syntactic dependencies emerge in lower layers and are gradually integrated into a coherent macro-syntactic structure in higher layers.
Furthermore, an analysis on a subject-verb agreement task shows that the timing of macro-structure formation is critical for performance, suggesting an optimal intermediate range for integrating global syntactic information.

Derivational Probing: Unveiling the Layer-wise Construction of Syntactic Structures in Neural Language Models

We analyze the syntactic sensitivity of Text-to-Speech (TTS) systems using methods inspired by psycholinguistic research. Specifically, we focus on the generation of intonational phrase boundaries, which can often be predicted by identifying syntactic boundaries within a sentence. We find that TTS systems struggle to accurately generate intonational phrase boundaries in sentences where syntactic boundaries are ambiguous (e.g., garden path sentences or sentences with attachment ambiguity). In these cases, systems need superficial cues such as commas to place boundaries at the correct positions. In contrast, for sentences with simpler syntactic structures, we find that systems do incorporate syntactic cues beyond surface markers. Finally, we finetune models on sentences without commas at the syntactic boundary positions, encouraging them to focus on more subtle linguistic cues. Our findings indicate that this leads to more distinct intonation patterns that better reflect the underlying structure.

A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivity

A common assumption in Computational Linguistics is that text representations learnt by multimodal models are richer and more human-like than those by language-only models, as they are grounded in images or audio—similar to how human language is grounded in real-world experiences. However, empirical studies checking whether this is true are largely lacking. We address this gap by comparing word representations from contrastive multimodal models vs. language only ones in the extent to which they capture experiential information—as defined by an existing norm-based 'experiential model'—and align with human fMRI responses. Our results indicate that, surprisingly, language-only models are superior to multimodal ones in both respects. Additionally, they learn more unique brain-relevant semantic information beyond that shared with the experiential model. Overall, our study highlights the need to develop computational models that better integrate the complementary semantic information provided by multimodal data sources.

Experiential Semantic Information and Brain Alignment: Are Multimodal Models Better than Language Models?

Recent work has argued that large language models (LLMs) are not "abstract reasoners", citing their poor zero-shot performance on a variety of challenging tasks as evidence. We revisit these experiments in order to add nuance to the claim. First, we show that while LLMs indeed perform poorly in a zero-shot setting, even tuning a small subset of parameters for input encoding can enable near-perfect performance. However, we also show that this finetuning does not necessarily transfer across datasets. We take this collection of empirical results as an invitation to (re-)open the discussion of what it means to be an "abstract reasoner", and why it matters whether LLMs fit the bill.

What is an "Abstract Reasoner"? Revisiting Experiments and Arguments about Large Language Models

Large language models (LLMs) have been deployed in a wide spectrum of domains and applications due to their strong language understanding capabilities obtained through pretraining. However, their performance on specific domain is usually suboptimal due to limited exposure to domain-specific tasks. Adapting LLM to the cinematic domain post unique challenges as it consists of complicated stories with limited textual information accessible from the subtitle or script alone. 

In this paper, we decompose the movie understanding capability into a suite of narrative understanding tasks based on narrative theory. We construct a dataset for these tasks based on resources in the movie domain, and use it to examine the effect of different domain adaptation strategies. Both the dataset and the models are made publicly available.
Our experiment results show the effectiveness of our approach in improving the narrative understanding of LLMs and highlight the trade-offs between domain-specific and general instruction capabilities.

Narrative understanding for Cinema domain large language model

Large Language Models (LLMs) have made significant contributions to cognitive science research. One area of application is narrative understanding. Sap et al. (2022) introduced $\textit{sequentiality}$, an LLM-derived measure that assesses the coherence of a story based on word probability distributions. They reported that recalled stories flowed less sequentially than imagined stories. However, the robustness and generalizability of this narrative flow measure remain unverified. To assess generalizability, we apply $\textit{sequentiality}$ derived from three different LLMs to a new dataset of matched autobiographical and biographical paragraphs. Contrary to previous results, we fail to find a significant difference in narrative flow between autobiographies and biographies. Further investigation reveals biases in the original data collection process, where topic selection systematically influences sequentiality scores. Adjusting for these biases substantially reduces the originally reported effect size. A validation exercise using LLM-generated stories with "good'' and "poor'' flow further highlights the flaws in the original formulation of sequentiality. Our findings suggest that LLM-based narrative flow quantification is susceptible to methodological artifacts. Finally, we provide some suggestions for modifying the $\textit{sequentiality}$ formula to accurately capture narrative flow.

From Stories to Statistics: Methodological Biases in LLM-Based Narrative Flow Quantification

In SemEval 2025 Task 9: The Food Hazard Detection Challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks:
(1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and
(2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product.
Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.

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Rubic2: Ensemble Model for Russian Lemmatization

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