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Yinong Chen

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2 papers

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EAAI Journal 2025 Journal Article

Thoughtful and cautious reasoning: A fine-tuned knowledge graph-based multi-hop question answering framework

Yinghao Zheng
Ling Lu
Yang Hu
Yinong Chen
Aijuan Wang

The aim of Knowledge Graph Question Answering (KGQA) is to find the answer entity by utilizing the Knowledge Graph (KG). Despite remarkable successes in recent years, the existing multi-hop KGQA research still faces numerous challenges. First, a multi-hop question often contains multiple entities and their relationships, and the semantic information is complex. The current methods extract the semantics of the question through an encoder that cannot completely extract the complex and rich semantic information in the multi-hop questions. Second, current question answering models use the coarse information filtering mechanism in the process of reasoning, which lead to the loss of effective information and introduce additional noise. To address these issues, we propose a Thoughtful and Cautious Reasoning framework for Knowledge Graph Question Answering (TCR-KGQA). We design a new question encoder that can extract and fully fuse the local semantic information of the question at different levels, focusing on the unique local features of the multi-hop question text. Based on the advantages of Gated Recurrent Unit (GRU) for information filtering, we propose a loop instruction update framework based on residual-GRU to effectively capture key information in the reasoning process. Extensive experiments on three broad benchmark datasets demonstrate the effectiveness of our model on KGQA tasks, and it also yields excellent results in the case of incomplete knowledge graphs with missing question–answer pairs.

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EAAI Journal 2024 Journal Article

DCDSum: An interpretable extractive summarization framework based on contrastive learning method

Jiaqi Zhang
Ling Lu
Liang Zhang
Yinong Chen
Wanping Liu

As the phenomenon of knowledge overload becomes more and more obvious, the automatic summarization technology still needs to break through the bottleneck in order to improve the application value and expand the scope of the application. Traditional training paradigms for extractive summarization systems suffer from the inconsistency in training and evaluation. In this paper, we propose an innovative and interpretable contrastive learning based framework for extractive summarization called DCDSum, which comprises a Diverse Oracle evaluator, a Contrastive learning extractor, and a Dynamic Top-k selector. Different from previous models that consider the extractive summarization task as a sequence labeling problem, our contrastive learning extractor treats it as a sentence reranking problem and introduces contrastive loss to achieve it, which can bridge the gap between objective function and evaluation metrics. The experimental results demonstrate the outstanding performance of our approach on the CNN/DailyMail, XSum, and PubMed datasets, achieving highly competitive results. In particular, our method achieves ROUGE-1 of 44. 65, ROUGE-2 of 21. 32, and ROUGE-L of 40. 87 on the CNN/DailyMail dataset. The outcomes across various evaluation metrics substantiate that the Diverse Oracle extraction algorithm adeptly captures a broader array of sentences with reduced redundancy, consequently enhancing the interpretability of the DCDSum framework.

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