Multimodal Model Editing (MMED) aims to correct erroneous knowledge in multimodal models. Existing evaluation methods, adapted from textual model editing, overstate success by relying on low-similarity or random inputs, obscure overfitting. We propose a comprehensive locality evaluation framework, covering three key dimensions: random-image locality, no-image locality, and consistent-image locality, operationalized through seven distinct data types, enabling a detailed and structured analysis of multimodal edits. We introduce De-VQA, a dynamic evaluation for visual question answering, uncovering a phenomenon we term transient blindness, overfitting to edit-similar text while ignoring visuals. Token analysis shows edits disproportionately affect textual tokens. We propose locality-aware adversarial losses to balance cross-modal representations. Empirical results demonstrate that our approach consistently outperforms existing baselines, reducing transient blindness and improving locality by 17% on average.

Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents. In the zero-shot setting, existing methods employ LLMs to generate synthetic data to address the challenge posed by the scarcity of annotated data. However, relying solely on Event-type-only prompts makes it difficult for the generated content to accurately capture the contextual and structural relationships of unseen events. Moreover, ensuring the reliability and usability of synthetic data remains a significant challenge due to the absence of quality evaluation mechanisms. To this end, we introduce a multi-agent collaboration framework for zero-shot document-level event argument extraction (ZS-DEAE), which simulates the human collaborative cognitive process of “Propose–Evaluate–Revise.” Specifically, the framework comprises a generation agent and an evaluation agent. The generation agent synthesizes data for unseen events by leveraging knowledge from seen events, while the evaluation agent extracts arguments from the synthetic data and assesses their semantic consistency with the context. The evaluation results are subsequently converted into reward signals, with event structure constraints incorporated into the reward design to enable iterative optimization of both agents via reinforcement learning. In three zero-shot scenarios constructed from the RAMS and WikiEvents datasets, our method achieves improvements both in data generation quality and argument extraction performance, while the generated data also effectively enhances the zero-shot performance of other DEAE models.

Yue Fan, Hu Zhang, Yunxiao Zhao, Guangjun Zhang, Hao Zhan, Ru Li, Hongye Tan, Yuanlong Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Ya Su, Hu Zhang, Dan Qiao, YuJie Wang, Yunxiao Zhao, Yue Fan, Shike Li, Ru Li, Hongye Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Despite significant advancements in large visual-language models (LVLMs), hallucinations remain a major bottleneck in their practical applications. One key factor contributing to hallucinations is the over-reliance on language priors during the autoregressive text generation process. Visual Contrastive Decoding (VCD), a popular technique for mitigating hallucinations, perturbs the visual input and compares the perturbed output with the original. However, it often overlooks the gradual attenuation of visual information within the decoder, limiting the model's ability to generate text based on actual visual content. We propose a novel, training-free method—Visual-Enhanced Contrastive Decoding (VECD)—which addresses this issue by amplifying visual information within the decoder, thereby reducing hallucinations caused by excessive reliance on language priors. VECD dynamically selects later layers for visual injection, while retaining only essential visual tokens in early layers. This approach enhances the generation process by adaptively balancing visual and language priors. By comparing outputs with and without visual amplification, we derive a refined probability distribution for the next token. Moreover, we improve the beam search algorithm by introducing a visually guided token selection strategy, enabling the generation of text that aligns more closely with the image content. Our extensive experiments show that VECD significantly reduces hallucinations and improves the quality of generated text, demonstrating its effectiveness as a practical solution.

Data-driven pre-trained language models typically perform shortcut learning wherein they rely on the spurious correlations between the data and the ground truth. This reliance can undermine the robustness and generalization of the model. To address this issue, data augmentation emerges as a promising solution. By integrating anti-shortcut data to the training set, the models’ shortcut-induced biases can be mitigated. However, existing methods encounter three challenges: 1) Manual definition of shortcuts is tailored to particular datasets, restricting generalization. 2) The inherent confirmation bias during model training hampers the effectiveness of data augmentation. 3) Insufficient exploration of the relationship between the model performance and the augmented data quantity may result in excessive data consumption. To tackle these challenges, we propose a method of Smart Data Augmentation based on Large Language Models (SAug-LLM). It leverages the LLMs to autonomously identify shortcuts and generate their anti-shortcut counterparts. In addition, the dual validation is employed to mitigate the confirmation bias during the model retraining. Furthermore, the data augmentation process is optimized to effectively rectify model biases while minimizing data consumption. We validate the effectiveness and generalization of our method through extensive experiments across various natural language processing tasks, demonstrating an average performance improvement of 5.61%.

Event Causal Identification (ECI) aims to identify fine-grained causal relationships between events from unstructured text. Contrastive learning has shown promise in enhancing ECI by optimizing representation distances between positive and negative samples. However, existing methods often rely on rule-based or random sampling strategies, which may introduce spurious causal positives. Moreover, static negative samples often fail to approximate actual decision boundaries, thus limiting discriminative performance. Therefore, we propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge Verification (DECLV). Specifically, we integrate multi-source knowledge validation and LLM-driven causal inference to construct a multi-stage knowledge validation mechanism, which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. Meanwhile, we introduce the Stochastic Gradient Langevin Dynamics (SGLD) method to dynamically generate adversarial negative samples, and employ an energy-based function to model the causal boundary between positive and negative samples. The experimental results show that our method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank.

Event Argument Extraction (EAE) aims to extract arguments for specified events from a text.Previous research has mainly focused on addressing long-distance dependencies of arguments, modeling co-occurrence relationships between roles and events, but overlooking potential inductive biases: (i) semantic differences among arguments of the same type and (ii) large margin separation between arguments of the different types.Inspired by prototype networks, we introduce a new model named HMPEAE, which takes the two inductive biases above as targets to locate prototypes and guide the model to learn argument representations based on these prototypes.Specifically, we set multiple prototypes to represent each role to capture intra-class differences.Simultaneously, we use hypersphere as the output space for prototypes, defining large margin separation between prototypes to encourage the model to learn significant differences between different types of arguments effectively.We solve the "argument-prototype" assignment as an optimal transport problem to optimize the argument representation and minimize the absolute distance between arguments and prototypes to achieve compactness within sub-clusters.Experimental results on the RAMS and WikiEvents datasets show that HMPEAE achieves state-of-the-art performances.

In low-texture scenes, Visual Odometry (VO) algorithms often encounter challenges stemming from sparse feature sets and reduced accuracy in feature matching. To overcome this, integrating plane features and vanishing point characteristics can provide additional constraints for refining camera poses. Optical flow-based tracking methods may also offer improved matching precision compared to traditional feature-based approaches. Motivated by these challenges, we present a robust Visual Odometry system tailored for low-texture environments. Our system combines a vanishing point-based approach for camera pose optimization with a Manhattan-aided algorithm for matching line segments using optical flow. By incorporating planes and vanishing points as supplementary features for pose estimation, we enhance overall accuracy without significant time overhead. We utilize detected line features to compute vanishing points, improving accuracy without compromising efficiency. In addition, our Manhattan-aided optical flow technique supplements and refines the results of line feature matching, further enhancing the accuracy of vanishing points. Evaluation on various public datasets demonstrates the superior accuracy and robustness of our system compared to state-of-the-art Simultaneous Localization And Mapping (SLAM) and VO methods. Notably, our method effectively addresses issues of failure in low-texture scenes and improves the accuracy of line feature matching compared to baseline methods. We will release our source code upon paper acceptance.

Document-level event extraction aims to identify event types and arguments from one document. However, existing methods fail to consider semantic distinctions between multiple mentions of one entity and ignore dynamic representation of entities across multiple events simultaneously. Therefore, the models cannot capture flexible and specific entity representations in different event types. In this article, we propose EADRE ( E vent-type- A ware D ynamic R epresentation of E ntities). Specifically, we use cross-attention between mentions and event-type prototypes to obtain event-type-aware mention features. Then, we propose ASGate ( A daptive S oft G ate), which adaptively selects mention features to reduce the influence of event-unrelated mentions. EADRE introduces no more than 1% new parameters compared with the base model and has good transportability. Experiments on two public datasets show that EADRE improves the performance of multi-event extraction by 2.6% and 3.1%, as well as outperforms previous state-of-the-art baselines by 0.2% and 1.6%, with lower resource consumption without the use of pre-trained models. Further experimental analysis shows that EADRE significantly improves extraction performance in O2M and M2M multi-event scenarios.

Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge.To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling.Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema.Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations.Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature.This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation.Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.

Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features from corpus, without relying on language specific resources is needed. In this paper, we develop a hybrid approach that combines Bidirectional long short-term memory (Bi-LSTM), word correlation degree calculation and weakly supervised K-means cluster to capture both sequence information and correlation degree of phrase from specific contexts, and use them to train a multi-word expression detector for multiple languages without any manually encoded features. Experiment result shows that the extraction results of Chinese and English multi-word expression using this hybrid approach is better than that of baseline algorithm, which verified that the hybrid approach is effective.

The automatic recognition of Chinese place names, a special case of the recognition of Chinese special nouns, is an important task in Chinese information processing. In this paper, we propose an approach combining statistical and rule-based techniques. The proposed approach discovers candidates from Chinese texts based upon the probability of a character being part of a Chinese place name; and confirms or eliminates the candidates by applying rules obtained by human summarization and transformation-based machine learning. In this approach, we employ a statistical measure: weight of likelihood (WOL), to estimate the likelihood of a character being part of a Chinese place name in real corpora. To the authors' knowledge, it is the first time WOL has been used to capture the capability of a character forming Chinese places names in real corpora. We evaluate the performance of our approach on a real data set and the recall and precision are 97% and 90.92% respectively.