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Aspect-Term Sentiment Analysis (ATSA) aims to predict sentiment polarity for specific aspect terms, a task complicated by conflicting sentiments and limited context in short texts. Existing graph-based approaches, including recent multi-graph methods, rely on predefined pairwise structures to capture different linguistic views. However, these approaches impose a fixed interaction granularity and depend on multiple predefined graphs, limiting their ability to adapt to sentence-specific sentiment composition and capture higher-order relationships. We propose HyperATSA, a dynamic hypergraph framework that constructs instance-specific aspect-opinion groupings directly from contextual token representations, without relying on external parsers. Hyperedges are induced via hierarchical agglomerative clustering over token embeddings, with a novel acceleration-based cutoff that identifies natural group boundaries per sentence. Experiments on Lap14, Rest14, and MAMS demonstrate consistent improvements over strong graph-based baselines, with controlled comparisons across BERT and RoBERTa backbones confirming that the gains stem from the hypergraph mechanism rather than encoder strength.

ACL 2026

From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

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.

Faithfulness in NLP explanations is typically assessed via input perturbations, implicitly treating explanations as causal drivers of decisions, an assumption ill-suited to human judgment. In educational assessment, explanations are better understood as structured justifications accompanying holistic decisions. We propose an explainability-first framework that treats human explanations as structured artifacts independent of prediction. By modeling explanation structure explicitly, our approach enables diagnostic analyses of emphasis, consistency, and dependence. We find that scores remain invariant under evidence removal, while explanatory structure persists in tutor-specific and rubric dependent ways, revealing a mismatch between prevailing faithfulness metrics and human reasoning. This provides a model-free foundation for auditing explanations and designing NLP systems whose explanations align with human evaluative practice.

Faithfulness Beyond Plausibility: Auditing Human Explanations in Educational Assessment

Although existing frameworks for large language model (LLM) inference on CPUs are mature, they fail to fully exploit the computational potential of many-core CPU platforms. Many-core CPUs are widely deployed in web servers and high-end networking devices, and are typically organized into multiple NUMA nodes that group cores and memory. Current frameworks largely overlook the substantial overhead of cross-NUMA memory access, limiting inference scalability and intelligence enabling on such platforms. To address this limitation, we build ArcLight, a lightweight LLM inference architecture designed from the ground up for many-core CPUs. ArcLight integrates efficient memory management and thread scheduling, and introduces finely controlled tensor parallelism to mitigate the cross-node memory access wall. Experimental results show that ArcLight significantly surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput. Moreover, ArcLight maintains compatibility with arbitrary CPU devices. ArcLight is publicly available at https://github.com/OpenBMB/ArcLight.

ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs

Public procurement data is legally open, yet practically locked inside thousands of unstructured PDFs and inconsistent portal metadata. pAtChWoRK starts with these fragmented, unstructured sources then leverages a hybrid pipeline (traditional NLP with LLM-based technologies) to restructure this information into a navigable knowledge base. Specifically, pAtChWoRK corrects manual classification errors, extracts complex unstructured fields such as award and solvency criteria and tenders' objectives, and assists users in easily navigating the tender landscape. This unified process enables more effective handling of the transparency bottlenecks that hinder competition and oversight in public administration. A user study with practitioners across different procurement

pAtChWoRK: Patching the Pieces of Public Procurement Documents

Vision-Language Models (VLMs) have become increasingly important in AI applications, yet interpretability tools for these models lag behind those available for text-only language models. While libraries like TransformerLens have enabled significant progress in understanding language models, existing tools for VLMs are limited to basic activation probing and saving. We introduce Spectra, a library specifically designed for mechanistic interpretability of VLMs that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. Built on HuggingFace's Transformers, our library handles architecture-specific complexities through per-checkpoint configurations while maintaining a simple, high-level interface. We demonstrate the library's capabilities by performing interpretability experiments on a counting task, showing how researchers can easily perform experiments that were previously cumbersome to do. The library currently supports Qwen2.5-VL, Qwen3-VL, LLaVA 1.5 and SmolVLM, with a design that facilitates extension to additional architectures. The library can be found at \url{github.com/clemneo/vlm-spectra}.

Spectra: A Mechanistic Interpretability Library for Vision-Language Models

We present OpenGlass, an open-source, privacy-preserving system for low-latency multimodal assistance in real-world deployments, with a primary focus on blind and low-vision users. Deploying large multimodal models for assistive use faces a practical tension between capability and interaction: cloud or server inference can provide strong visual understanding but often incurs multi-second delays and network jitter, while wearable devices can continuously sense the environment but have limited compute and power budgets. OpenGlass resolves this gap with a split local-first design: a lightweight glasses-side sensing unit captures visual context, while a nearby consumer-grade device performs multimodal inference and streams concise spoken feedback over local Wi-Fi or cellular connections, avoiding reliance on remote cloud services. We introduce a system-level evaluation suite covering response quality, end-to-end latency from query completion to audio playback, and safety-first abstention with auditable logs. OpenGlass supports three representative interaction patterns: mobile scene guidance for obstacle avoidance, user initiated interruption of speech output, and multi-turn question answering grounded in the current scene. In real deployment on consumer-grade hardware, OpenGlass achieves sub-2s system-level end-to-end latency from query completion to first audio playback.

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

Large language models (LLMs) have shown remarkable potential in scientific hypothesis discovery. However, existing approaches treat divergent exploratory ideation and convergent fine-grained refinement as isolated tasks, lacking a principled mechanism for human guidance. We present MOOSE-Copilot, the first unified framework to bridge this abstraction gap through a formalized human–AI interaction protocol. Our system empowers scientists to actively steer the automated process via three explicit guiding signals: initial blueprints, inter-stage routing, and regenerative feedback. Quantitative evaluations demonstrate that injecting these structured expert signals significantly outperforms purely autonomous baselines, establishing a theoretical upper bound for hypothesis quality through iterative refinement. Furthermore, we operationalize this paradigm into an accessible web-based interface featuring interactive tree visualization. By reducing the cognitively demanding discovery process into manageable subtasks of conceptual selection and critique, MOOSE-Copilot enables interdisciplinary researchers to effectively orchestrate and accelerate end-to-end scientific breakthroughs.

MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory- and Fine-Grained Scientific Hypothesis Discovery

Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architectural modifications or optimizer adjustments. However, these approaches lack systematic optimization of weight patterns during training. Weight pattern refers to the distribution and relative magnitudes of weight parameters in a neural network. To address this issue, we propose a Weight Scaling method called WISCA to enhance training efficiency and model quality by strategically improving neural network weight patterns—without changing network structures. By rescaling weights while preserving model outputs, WISCA indirectly optimizes the model’s training trajectory. Experiments demonstrate that WISCA significantly improves convergence quality (measured by generalization capability and loss reduction), particularly in LLMs with Grouped Query Attention (GQA) architectures and LoRA fine-tuning tasks. Empirical results show 5.6% average improvement on zero-shot validation tasks and 2.12% average reduction in training perplexity across multiple architectures.

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling

Role-playing models (RPMs) are widely used in real-world applications but underperform when deployed in the wild. This degradation can be attributed to distribution shifts, including user, character, and dialogue compositional shifts. Existing methods like LLM-as-a-judge fall short in providing a fine-grained diagnosis of how these shifts affect RPM generalization, and thus there lack formal frameworks to characterize RPM generalization behaviors. To bridge these gaps, we introduce an information-theoretic metric, named reasoning-based effective mutual information difference (R-EMID), to measure RPM performance degradation in an interpretable way. We also derive an upper bound on R-EMID to predict the worst-case generalization performance of RPMs and theoretically reveal how various shifts contribute to the RPM performance degradation. Moreover, we propose a co-evolving reinforcement learning framework to adaptively model the connection among user, character, and dialogue context and thus enhance the estimation of dialogue response generation probability, which is critical for calculating R-EMID. Finally, we evaluate the generalization performance of various RPMs using R-EMID, finding that user shift poses the highest risk among all shifts and reinforcement learning is the most effective approach for enhancing RPM generalization. Code and data are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/RPM-Generalization.

Understanding Generalization in Role-Playing Models via Information Theory

Live-stream E-commerce faces significant challenges from morphs, deliberate linguistic variants used to evade real-time voice filters and amplify product claims illegally. While critical for regulatory enforcement, Live Auditory Morph Resolution (LiveAMR) research is hindered by limited datasets: prior work relied on narrow, redundant health domain corpora, restricting model robustness. To bridge this gap, we introduce two datasets: (1) HealthAMR, a refined health-domain corpus via deduplication and re-annotation. (2) GeneralAMR, a general domain benchmark with 28K annotated sentences from 77 channels across 7 E-commerce categories. Further, we propose JointMRE, a multi-task framework that jointly resolves morphs and generates structured explanations, transferring grammatical insights from large language models to enhance generalization. Predictions are refined by our Conflict-aware Dual-output Refinement Framework (CDRF), which detects inconsistencies between corrections and explanations. Experiments show CDRF significantly improves morph resolution accuracy and interpretability. Our datasets and code are available \footnote{\url{https://anonymous.4open.science/r/Morph-Resolution-Datasets-and-Methods-611E}}.

Chinese Live‑Streaming E‑Commerce Morph Resolution: Datasets and Methods

We introduce AVACraft — the first multimodal benchmark environment for complex decision-making in StarCraft II, supporting both traditional Multi-Agent Reinforcement Learning (MARL) and modern Vision-Language Model (VLM) paradigms. Existing StarCraft II environments like SMAC rely on abstract state representations that deviate from human perception and lack support for emerging VLM-based decision-making. AVACraft mitigates these limitations via a unified framework, which provides RGB visual inputs, natural language observations and structured state information, enabling systematic comparisons between training-based and zero-shot decision-making methods. Our benchmark features 21 carefully designed scenarios covering micromanagement, coordination and strategic planning, with standardized evaluation protocols for both paradigms. We establish comprehensive baselines using four MARL algorithms (IQL, QMIX, QTRAN, VDN) and multiple state-of-the-art VLMs (GPT-4o, Qwen-VL, etc.). Experimental results reveal their complementary strengths: MARL methods achieve up to 27.1% win rate after 1M training steps in complex scenarios, while VLMs deliver superior zero-shot performance (75–81% win rate) and human-aligned decision processes without any training. Systematic analysis (including expert human evaluation) also identifies key trade-offs between training efficiency, performance ceilings and interpretability across the two paradigms. Our implementation is available at https://anonymous.4open.science/r/VLM-Play-StarCraft2-70C4 .

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Faithfulness Beyond Plausibility: Auditing Human Explanations in Educational Assessment

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Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification

Stay up to date with the latest Underline news!

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