Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Dongming Sheng, Kexin Han, Hao Li, Yan Zhang, Yucheng Huang, Jun Lang, Wenqiang Liu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Multi-entity collaborative relationship extraction is an important but challenging task, which has been attracting a lot of interest and poses significant issues in front of systems aimed at natural language understanding. Instead of designing specific models for single relationship extraction tasks, this paper aims to propose a general framework to extract multiple relations among multiple entities in unstructured text by taking advantage of existing models. Based on performing named entity recognition and relation extraction collaboratively, the framework exploits correlations and information propagation among words and relations in a graph network to grasp fundamental features for final classification. The experimental results on two real-world datasets demonstrate that our framework has remarkable applicability and generalizability, and consistently outperforms the strong competitors by a noticeable margin for multi-entity relation extraction.

Existing works for Dialogue Act Recognition (DAR) pay little attention to the imbalanced distribution of the Dialogue Acts (DAs) and exclusively train their models over very fine-grained DAs in one pass, which leads to a limited performance in recognizing low-frequent DAs. To address this issue, we propose a hierarchical label structured network that explicitly introduces coarse-grained DAs to the original fine-grained DAs. A two-pass multi-head attention mechanism is devised to integrate different levels of DA information into the utterance encoding process, thereby utilizing the information learned from the coarse-grained DAs to guide the recognition of the target fine-grained DAs. Besides, a transfer learning over large-scale dialogue datasets is also employed to further boost label representation of the coarse-grained DAs. Extensive experiments show that our model significantly outperforms the state-of-the-art methods, verifying the effectiveness of integrating the hierarchical structure among DAs.

Automatic question generation from a sentence or paragraph is a challenging task. Recently, impressive progress has been made, mainly owing to the advance of deep neural networks. However, existing neural approaches tend to simply copy words from the given answer, making the generated question equivocal and informatively redundant. In this work, we propose an edit-based network, named Multi-stage Answers Editing Network (MultiEdit), to generate the question in the three-stage operations. In the initial stage, we convert the answer into an edit vector and then combine the edit vector and sentence representation to initialize the decoder, which helps choose an interrogative word matching the answer type. In the second stage, we edit the contextual representation by computing the weighted average between the context representation and the edit vector. In the final stage, we adopt the additional reinforcement learning to improve the effectiveness of generated semantics and evaluation indicators. Experimental results on both Chinese and English datasets show that our approach outperforms the state-of-the-art methods in terms of both automatic and human evaluation.

Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications.Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances.Phrases carry sentiments when they are referred to emotional events under certain topics, providing a global semantic connection between utterances throughout the entire conversation.In this work, we propose a two-stage Summarization and Aggregation Graph Inference Network (SumAggGIN), which seamlessly integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion.Topic-related emotional phrases, which constitutes the global topic-related emotional connections, are recognized by our proposed heterogeneous Summarization Graph.Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases.The two steps of graph inference are tightly-coupled for a comprehensively understanding of emotional fluctuation.Experimental results on three CER benchmark datasets verify the effectiveness of our proposed model, which outperforms the stateof-the-art approaches.