Lu Xiang


2022

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Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Haitao Lin | Junnan Zhu | Lu Xiang | Yu Zhou | Jiajun Zhang | Chengqing Zong
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role’s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles’ content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles’ critical information. The cross attention interaction aims to select other roles’ critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles’ summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles’ content could help generate summaries with more complete semantics and correct topic structures.

2021

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CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Haitao Lin | Liqun Ma | Junnan Zhu | Lu Xiang | Yu Zhou | Jiajun Zhang | Chengqing Zong
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Dialogue summarization has drawn much attention recently. Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowing customer’s issues and service progress. These applications require summaries to contain the perspective of a single speaker and have a clear topic flow structure, while neither are available in existing datasets. Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS). CSDS improves the abstractive summaries in two aspects: (1) In addition to the overall summary for the whole dialogue, role-oriented summaries are also provided to acquire different speakers’ viewpoints. (2) All the summaries sum up each topic separately, thus containing the topic-level structure of the dialogue. We define tasks in CSDS as generating the overall summary and different role-oriented summaries for a given dialogue. Next, we compare various summarization methods on CSDS, and experiment results show that existing methods are prone to generate redundant and incoherent summaries. Besides, the performance becomes much worse when analyzing the performance on role-oriented summaries and topic structures. We hope that this study could benchmark Chinese dialogue summarization and benefit further studies.

2020

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A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization
Jinghui Yan | Yining Wang | Lu Xiang | Yu Zhou | Chengqing Zong
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures.

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Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Yang Zhao | Lu Xiang | Junnan Zhu | Jiajun Zhang | Yu Zhou | Chengqing Zong
Proceedings of the 28th International Conference on Computational Linguistics

Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized. ii) Granularity mismatch: the current KG methods utilize the entity as the basic granularity, while NMT utilizes the sub-word as the granularity, making the KG different to be utilized in NMT. To alleviate above problems, we propose a multi-task learning method on sub-entity granularity. Specifically, we first split the entities in KG and sentence pairs into sub-entity granularity by using joint BPE. Then we utilize the multi-task learning to combine the machine translation task and knowledge reasoning task. The extensive experiments on various translation tasks have demonstrated that our method significantly outperforms the baseline models in both translation quality and handling the entities.

2014

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Word Segmenter for Chinese Micro-blogging Text Segmentation – Report for CIPS-SIGHAN’2014 Bakeoff
Lu Xiang | Xiaoqing Li | Yu Zhou
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing