United States

Existing discourse corpora annotated under different frameworks adopt distinct but somewhat related taxonomies of relations. How to integrate discourse frameworks has been an open research question. Previous studies on this topic are mainly theoretical, although such research is typically performed with the hope of benefiting computational applications. In this paper, we show how the proposal by Sanders et al. (2018) based on the Cognitive approach to Coherence Relations (CCR) (Sanders et al.,1992, 1993) can be used effectively to facilitate cross-framework discourse relation (DR) classification. To address the challenges of using predicted UDims for DR classification, we adopt the Bayesian learning framework based on Monte Carlo dropout (Gal and Ghahramani, 2016) to obtain more robust predictions. Data augmentation enabled by our proposed method yields strong performance (55.75 for RST and 55.01 for PDTB implicit DR classification in macro-averaged F1). We compare four model designs and analyze the experimental results from different perspectives. Our study shows an effective and cross-framework generalizable approach for DR classification, filling a gap in existing studies.

NAACL 2025

Cross-Framework Generalizable Discourse Relation Classification Through Cognitive Dimensions

cognitive dimensions of discourse relations

cross-framework

discourse relation classification

pdtb

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Voice-based AI development faces unique challenges in processing both linguistic and paralinguistic information. This study compares how large audio-language models (LALMs) and humans integrate speaker characteristics during speech comprehension, asking whether LALMs process speaker-contextualized language in ways that parallel human cognitive mechanisms. We compared two LALMs' (Qwen2-Audio and Ultravox 0.5) processing patterns with human EEG responses. Using surprisal and entropy metrics from the models, we analyzed their sensitivity to speaker-content incongruency across social stereotype violations (e.g., a man claiming to regularly get manicures) and biological knowledge violations (e.g., a man claiming to be pregnant). Results revealed that Qwen2-Audio exhibited increased surprisal for speaker-incongruent content and its surprisal values significantly predicted human N400 responses, while Ultravox 0.5 showed limited sensitivity to speaker characteristics. Importantly, neither model replicated the human-like processing distinction between social violations (eliciting N400 effects) and biological violations (eliciting P600 effects). These findings reveal both the potential and limitations of current LALMs in processing speaker-contextualized language, and suggest differences in social-linguistic processing mechanisms between humans and LALMs.

Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment

Speech errors are a natural part of communication, yet they rarely lead to complete communicative failure because both speakers and comprehenders can detect and correct errors. Although prior research has examined error monitoring and correction in production and comprehension separately, integrated investigation of both systems has been impeded by the scarcity of parallel data. In this study, we present SPACER, a parallel dataset that captures how naturalistic speech errors are corrected by both speakers and comprehenders. We focus on single-word substitution errors extracted from the Switchboard speech corpus, accompanied by speaker's self-repairs and comprehenders' responses from an offline text-editing experiment. Our exploratory analysis suggests asymmetries in error correction strategies: speakers are more likely to repair errors that introduce greater semantic and phonemic deviations, whereas comprehenders tend to correct errors that are phonemically similar to more plausible alternatives or do not fit into prior contexts. Our dataset enables future research on the integrated approach of language production and comprehension.

SPACER: A Parallel Dataset of Speech Production And Comprehension of Error Repairs

Humans deal with temporary syntactic ambiguity all the time in incremental sentence processing. Sentences with temporary ambiguity that causes processing difficulties, often reflected by increase in reading time, are referred to as garden-path sentences. Garden-path theories of sentence processing attribute the increases in reading time to the reanalysis of the previously ambiguous syntactic structure to make it consistent with the new disambiguating text. It is unknown whether transformer-based language models successfully resolve the temporary ambiguity after encountering the disambiguating text. We investigated this question by analyzing completions generated from language models for a type of garden-path sentence with ambiguity between a complement clause interpretation and a relative clause interpretation. We found that larger language models are worse at resolving such ambiguity.

Are Larger Language Models Better at Disambiguation?

In cases of pervasive uncertainty, cognitive systems benefit from heuristics or committing to more general hypotheses. Here we have presented a hierarchical cognitive model of lexical processing that synthesizes advances in early rational cognitive models with modern-day neural architectures. Probabilities of higher-order categories derived from layers extracted from the middle layers of an encoder language model have predictive power in accounting for several reading measures for both predicted and unpredicted words and influence even early first fixation duration behavior. The results suggest that lexical processing can take place within a latent, but nevertheless discrete, space in cases of uncertainty.

Towards a Bayesian hierarchical model of lexical processing

LLM-surprisal is a computational measure of how unexpected a word or character is given the preceding context, as estimated by large language models (LLMs). This study investigated the effectiveness of LLM-surprisal in modeling second language (L2) writing development, focusing on Chinese L2 writing as a case to test its cross-linguistical generalizability. We selected three types of LLMs with different pretraining settings: a multilingual model trained on various languages, a Chinese-general model trained on both Simplified and Traditional Chinese, and a Traditional-Chinese-specific model. This comparison allowed us to explore how model architecture and training data affect LLM-surprisal estimates of learners’ essays written in Traditional Chinese, which in turn influence the modeling of L2 proficiency and development. We also correlated LLM-surprisals with 16 classic linguistic complexity indices (e.g., character sophistication, lexical diversity, syntactic complexity, and discourse coherence) to evaluate its interpretability and validity as a measure of L2 writing assessment. Our findings demonstrate the potential of LLM-surprisal as a robust, interpretable, cross-linguistically applicable metric for automatic writing assessment and contribute to bridging computational and linguistic approaches in understanding and modeling L2 writing development. All analysis scripts are available at https://github.com/JingyingHu/ChineseL2Writing-Surprisals.

Modeling Chinese L2 Writing Development: The LLM-Surprisal Perspective

Animacy is a well-documented factor affecting language production, but its influence on Language Models (LMs) in complex structures like Object Relative Clauses (ORCs) remains underexplored. This study examines LMs' sensitivity to animacy in English ORC structure choice (passive vs. active) using surprisal-based and prompting-based analyses, alongside human baselines. In surprisal-based analysis, DistilGPT-2 best mirrored human preferences, while GPT-Neo and BERT-base showed rigid biases, diverging from human patterns. Prompting-based analysis expanded testing to GPT-4o-mini, Gemini models, and DeepSeek-R1, revealing GPT-4o-mini’s stronger human alignment but limited animacy sensitivity in Gemini models and DeepSeek-R1. Some LMs exhibited inconsistencies between analyses, reinforcing that prompting alone is unreliable for assessing linguistic competence. Corpus analysis confirmed that training data alone cannot fully explain animacy sensitivity, suggesting emergent animacy-aware representations. These findings underscore the interaction between training data, model architecture, and linguistic generalization, highlighting the need for integrating structured linguistic knowledge into LMs to enhance their alignment with human sentence processing mechanisms.

Beyond Binary Animacy: A Multi-Method Investigation of LMs’ Sensitivity in English Object Relative Clauses

Language syllabification is the separation of a word into written or spoken syllables. The study of syllabification plays a pivotal role in morphology and there have been previous attempts to study this phenomenon using graphs or networks. Previous approaches have claimed through visual estimation that the degree distribution of language networks follows the Power Law distribution, however, there have not been any empirically grounded metrics to determine the same. In our study, we implement two kinds of language networks, namely, syllabary and lexical networks, and investigate the syllabification of four European languages: English, French, German and Spanish using network analysis and examine their small-world, random and scale-free nature. We additionally empirically prove that contrary to claims in previous works, although the degree distribution of these networks appear to follow a power law distribution, they are actually more in agreement with a log-normal distribution, when a numerically grounded curve-fitting is applied. Finally, we explore how syllabary and lexical networks for the English language change over time using a database of age-of-acquisition rating words. Our analysis further shows that the preferential attachment mechanism appears to be a well-grounded explanation for the degree distribution of the syllabary network.

An Empirical Study of Language Syllabification using Syllabary and Lexical Networks

This paper investigates good-enough parsing in Turkish by comparing human self-paced reading performance to the surprisal and attention patterns of three Turkish Large Language Models (LLMs), GPT-2-Base, GPT-2-Large, and LLaMA-3. The results show that Turkish speakers rely on good-enough parsing for implausible but grammatically permissible sentences (e.g., interpreting sentences such as `the man bit the dog' as 'the dog bit the man'). Although the smaller LLMs (e.g., GPT-2) were better predictors of human RTs, they seem to have relied more heavily on semantic plausibility than humans. Comparably, larger LLMs (e.g., LLaMA-3) tended to make more probabilistic parsing based on word order, exhibiting less *good-enough* parsing behavior. Therefore, we conclude that LLMs take syntactic and semantic constraints into account when processing thematic roles, but not to the same extent as human parsers.

When Men Bite Dogs: Testing Good-Enough Parsing in Turkish with Humans and Large Language Models

Humans tend to predict the next words during sentence comprehension, but under unique circumstances, they demonstrate an ability for longer coherent word sequence prediction. In this paper, we investigate whether Transformers can model such hyperprediction observed in humans during sentence processing, specifically in the context of Japanese buzzer quizzes. We conducted eye-tracking experiments where the participants read the first half of buzzer quiz questions and predicted the second half, while we modeled their reading time using the GPT-2. By modeling the reading times of each word in the first half of the question using GPT-2 surprisal, we examined under what conditions fine-tuned language models can better predict reading times. As a result, we found that GPT-2 surprisal effectively explains the reading times of quiz experts as they read the first half of the question while predicting the latter half. When the language model was fine-tuned with quiz questions, the perplexity value decreased. Lower perplexity corresponded to higher psychometric predictive power; however, excessive data for fine-tuning led to a decrease in perplexity and the fine-tuned model exhibited a low psychometric predictive power. Overall, our findings suggest that a moderate amount of data is required for fine-tuning in order to model human hyperprediction.

Transformers Can Model Human Hyperprediction in Buzzer Quiz

This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs). Using Mandarin ditransitive constructions — double object (DO, “She gave the girl the candy, but not…”) vs. prepositional object (PO, “She gave the candy to the girl, but not…”) as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5). Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., “the candy”), contrast predictions were significantly modulated by sentence structure—particularly in spoken contexts, where the sentence-final element drew more attention. (2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions. By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on human–model alignment in linguistic tasks.

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