Xia Zhang


Guiding Neural Entity Alignment with Compatibility
Bing Liu | Harrisen Scells | Wen Hua | Guido Zuccon | Genghong Zhao | Xia Zhang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods. To power neural EA models with compatibility, we devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; (3) how to optimise parameters of the compatibility model. Extensive experiments on widely-used datasets demonstrate the advantages of integrating compatibility within EA models. In fact, state-of-the-art neural EA models trained within our framework using just 5% of the labelled data can achieve comparable effectiveness with supervised training using 20% of the labelled data.

A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction
Yuan Xia | Zhenhui Shi | Jingbo Zhou | Jiayu Xu | Chao Lu | Yehui Yang | Lei Wang | Haifeng Huang | Xia Zhang | Junwei Liu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

With the development of medical digitization, the extraction and structuring of Electronic Medical Records (EMRs) have become challenging but fundamental tasks. How to accurately and automatically extract structured information from medical dialogues is especially difficult because the information needs to be inferred from complex interactions between the doctor and the patient. To this end, in this paper, we propose a speaker-aware co-attention framework for medical dialogue information extraction. To better utilize the pre-trained language representation model to perceive the semantics of the utterance and the candidate item, we develop a speaker-aware dialogue encoder with multi-task learning, which considers the speaker’s identity into account. To deal with complex interactions between different utterances and the correlations between utterances and candidate items, we propose a co-attention fusion network to aggregate the utterance information. We evaluate our framework on the public medical dialogue extraction datasets to demonstrate the superiority of our method, which can outperform the state-of-the-art methods by a large margin. Codes will be publicly available upon acceptance.


SXUCFN-Core: STS Models Integrating FrameNet Parsing Information
Sai Wang | Ru Li | Ruibo Wang | Zhiqiang Wang | Xia Zhang
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity