United States

Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localization of non-verbal events remains a critical yet under-explored challenge. Current methods suffer from insufficient task definitions with limited category coverage and ambiguous temporal granularity. They also lack standardized evaluation frameworks, hindering the development of downstream applications. To bridge this gap, we first develop a refined taxonomy of 21 vocal events, with a new categorization into discrete (standalone) versus continuous (mixed with speech) types. Based on the refined taxonomy, we introduce WESR-Bench, an expert-annotated evaluation set (900+ utterances) with a novel position-aware protocol that disentangles ASR errors from event detection, enabling precise localization measurement for both discrete and continuous events. We also build a strong baseline by constructing a 1,700+ hour corpus, and train specialized models, surpassing both open-source audio-language models and commercial APIs while preserving ASR quality. We anticipate that WESR will serve as a foundational resource for future research in modeling rich, real-world auditory scenes.

ACL 2026

WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

poster

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Reasoning large language models (LLMs) have recently made much progress in complex problem-solving, leveraging internal reasoning (or thought) to guide their solution generation. However, existing LLM-based counseling agents, including those using Motivational Interviewing (MI), generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness. We propose MIThinker, a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation. To overcome the lack of annotated thought data, we introduce AugR1-MI, an automated pipeline that reverse-engineers counselor's thoughts from observed responses. Through two-stage training combining supervised fine-tuning and reinforcement learning, MIThinker demonstrates improved theory-of-mind assessment and strategy alignment. Comprehensive evaluations show that MindfulMI, our agent leveraging MIThinker, achieves MI competency comparable to state-of-the-art systems with an order of magnitude less computation.

MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling

Learning Bayesian Networks typically requires access to raw tabular data to estimate conditional probabilities. However, in many scientific domains, raw data is unavailable due to privacy concerns or general lack of access, while structured statistical summaries are increasingly accessible through large language models and published literature. We propose and evaluate five distinct strategies to reconstruct local conditional probability tables solely from statistical summaries in order to parameterize Bayesian Networks. Our comprehensive evaluation across mixed-type synthetic networks demonstrates that copula-based methods significantly outperform standard baselines, offering a viable path for knowledge integration from heterogeneous sources -- unlocking the wealth of published knowledge for causal modeling while ensuring transparency and verifiability.

Verifiable Parameterization of Bayesian Networks from Scientific Literature: Unlocking Unstructured Empirical Evidence

Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capabilities of the original models. In this work, we propose PRIME, an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two complementary stages. First, ultra-low-rank principal task vector merging retains only a small fraction of singular vectors, effectively reducing task interference while preserving most of the task-specific performance. Second, orthogonal residual task vector merging incorporates the remaining components by projecting them onto the null space of the principal subspace, thereby avoiding interference while recovering additional task-relevant information. Extensive experiments on eight natural language processing tasks demonstrate that PRIME consistently outperforms existing model merging methods, achieving improvements of up to 1.18% on T5 and 1.9% on LLaMA-3.2-3B.

 PRIME: Ultra-Low-Rank Principal–Residual Model Merging

Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, explicitly encoded bias. We demonstrate that this approach dramatically underestimates real-world bias by examining a more realistic task: generating machine learning (ML) pipelines. Testing both code-specialized and general-instruction large language models, we find that ML pipelines exhibit substantially greater bias than simple conditionals across all conditions: standard generation, with varying prompt-based mitigation strategies, varying numbers of attributes, and different ML pipeline difficulty levels. Even attribute selection alone, the simplest pipeline difficulty, shows higher bias compared to conditionals, demonstrating that ML pipelines inherently amplify bias beyond what isolated conditionals reveal. Critically, we uncover a stark asymmetry: models maintain equivalent bias detection performance on both simple conditionals and ML pipelines, revealing that models recognize bias equally well in both contexts yet generate significantly more biased code in ML pipelines. These findings challenge simple conditionals as valid proxies for bias evaluation and suggest current benchmarks mischaracterize model safety in practical deployment contexts.

 From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning–answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models

In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn conversation, they cannot maintain the required speaking styles after several turns of interaction; we refer to this as the \textit{style amnesia} of SLMs. We focus on paralinguistic speaking styles, including emotion, accent, volume, and speaking speed. We evaluate three proprietary and two open-source SLMs, demonstrating that none of these models can maintain a consistent speaking style when instructed to do so. We further show that while SLMs can recall the style instruction when prompted in later turns, they still fail to express it, but through explicit recall can mitigate \textit{style amnesia}. In addition, SLMs struggle more when the style instruction is placed in system messages rather than user messages, even though system messages are specifically designed to provide persistent, conversation-level instructions. Our findings highlight a systematic gap in current SLMs' ability to maintain speaking styles, highlighting the need for improved style adherence in future models. Our code and evaluation data are publicly available at https://github.com/YuXiangLin1234/SLM-Style-Amnesia.

Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegance, its implementation in practice is complex, limiting its adoption to the SentencePiece package and adapters thereof. We bridge this gap between theory and practice by providing a clear guide to implementation and parameter choices. We also identify a simpler algorithm that accepts slightly higher training loss in exchange for improved compression.

Which Pieces Does Unigram Tokenization Really Need?

Audio language models (ALMs) are increasingly used for speech-based understanding; yet, their ability to perform semantic reasoning beyond transcription, Text-to-Audio Retrieval, Captioning, and Question-Answering accuracy remains insufficiently benchmarked. In particular, the effects of accent variation, domain shift, and semantic over-inference on audio reasoning are poorly understood. We evaluate audio language models across five semantic and paralinguistic reasoning tasks: entailment, consistency, plausibility, accent drift, and accent restraint. Collectively, these tasks assess a model’s ability to reason over spoken audio as the primary evidence source, including whether a textual hypothesis can be inferred, contradicted, or left undetermined by the audio, whether statements align or conflict with spoken content, whether claims are plausible given the discourse, and whether model predictions remain stable or appropriately constrained across accent variation. These findings highlight critical limitations in current audio reasoning evaluations and hope to provide guidance for more robust and equitable ALM design and assessment.

Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents

Chain-of-Thought (CoT) reasoning, while effective, suffers from an inherent mechanism flaw: linearity induces overthinking. Constrained by sequential generation, models often produce redundant narration and circular self-corrections to maintain logical context. We propose GoT-R1, a framework that fundamentally mitigates this by replacing verbose linear trajectories with high-density reasoning graphs. Unlike CoT, GoT-R1 decouples logic from narration, modeling deliberation as a structured topology of atomic units. We internalize this inductive bias via a two-stage regimen: synthesizing structural data to distill logical skeletons, followed by Group Relative Policy Optimization (GRPO) to explicitly reinforce topological integrity. Extensive evaluations across mathematical reasoning and instruction following demonstrate that GoT-R1 consistently outperforms state-of-the-art baselines. Crucially, it achieves these gains with significantly reduced token overhead, demonstrating that structured reasoning density offers a more robust and parsimonious alternative to the recursive verbosity of standard CoT. The GoT-R1 models are open-sourced on Hugging Face at: https://huggingface.co/collections/MYTH-Lab/got-r1.

GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning

Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an effective alternative for testing and advancing legal theory, as well as supporting legal administration. Large Language Models (LLMs), with their world knowledge and role-playing capabilities, are strong candidates to serve as the foundation for legal society simulation. However, the application of LLMs to simulate legal systems remains underexplored. In this work, we introduce **Law in Silico**, a unified LLM-based agent framework for simulating legal scenarios that incorporate individual decision-making and institutional mechanisms, such as legislation, adjudication, and enforcement. We calibrate agent behaviors against real-world crime data, demonstrating that LLM-based agents can capture realistic sociological correlations. Building on this foundation, we structure our simulation through a ''Micro-to-Macro'' process: we conduct micro-level simulations in representative conflict-driven scenarios, allowing legal rules to evolve through agent-institution interactions naturally. These evolved laws are then deployed back into macro-scale populations to evaluate their effectiveness in regulating behaviors. Through comprehensive experiments, our results reveal that a well-functioning, transparent, and adaptive legal system can mitigate "cat-and-mouse" regulatory dynamics and offer better protection for vulnerable individuals.

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MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling

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