Yijian Tian


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Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots
Xin Lu | Yijian Tian | Yanyan Zhao | Bing Qin
Findings of the Association for Computational Linguistics: EMNLP 2021

Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrieval-based chatbots is still in the early stage. Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-and-rewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-the-shelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.


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An Iterative Emotion Interaction Network for Emotion Recognition in Conversations
Xin Lu | Yanyan Zhao | Yang Wu | Yijian Tian | Huipeng Chen | Bing Qin
Proceedings of the 28th International Conference on Computational Linguistics

Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.