Zhuoran Wang


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A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation
Zongsheng Wang | Yunzhi Bai | Bowen Wu | Zhen Xu | Zhuoran Wang | Baoxun Wang
Proceedings of the 27th International Conference on Computational Linguistics

Generative dialog models usually adopt beam search as the inference method to generate responses. However, small-width beam search only focuses on the limited current optima. This deficiency named as myopic bias ultimately suppresses the diversity and probability of generated responses. Although increasing the beam width mitigates the myopic bias, it also proportionally slows down the inference efficiency. To alleviate the myopic bias in small-width beam search, this paper proposes a Prospective-Performance Network (PPN) to predict the future reward of the given partially-generated response, and the future reward is defined by the expectation of the partial response appearing in the top-ranked responses given by a larger-width beam search. Enhanced by PPN, the decoder can promote the results with great potential during the beam search phase. The experimental results on both Chinese and English corpora show that our method is promising to increase the quality and diversity of generated responses, with inference efficiency well maintained.

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LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
Zhen Xu | Nan Jiang | Bingquan Liu | Wenge Rong | Bowen Wu | Baoxun Wang | Zhuoran Wang | Xiaolong Wang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.


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Group Linguistic Bias Aware Neural Response Generation
Jianan Wang | Xin Wang | Fang Li | Zhen Xu | Zhuoran Wang | Baoxun Wang
Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing

For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.

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Neural Response Generation via GAN with an Approximate Embedding Layer
Zhen Xu | Bingquan Liu | Baoxun Wang | Chengjie Sun | Xiaolong Wang | Zhuoran Wang | Chao Qi
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a “safe responses”), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.

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Predicting Users’ Negative Feedbacks in Multi-Turn Human-Computer Dialogues
Xin Wang | Jianan Wang | Yuanchao Liu | Xiaolong Wang | Zhuoran Wang | Baoxun Wang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

User experience is essential for human-computer dialogue systems. However, it is impractical to ask users to provide explicit feedbacks when the agents’ responses displease them. Therefore, in this paper, we explore to predict users’ imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions. To our knowledge, this is the first work focusing on this task. Several possible factors that trigger negative emotions are modelled. A relation sequence model (RSM) is proposed to encode the sequence of appropriateness of current response with respect to the earlier utterances. The experimental results show that the proposed structure is effective in modelling emotional risk (possibility of negative feedback) than existing conversation modelling approaches. Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work. Balanced sampling with respect to the last response in the distance supervision data are shown to be reliable for data augmentation.


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Learning Domain-Independent Dialogue Policies via Ontology Parameterisation
Zhuoran Wang | Tsung-Hsien Wen | Pei-Hao Su | Yannis Stylianou
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue


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Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System
Zhuoran Wang | Hongliang Chen | Guanchun Wang | Hao Tian | Hua Wu | Haifeng Wang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information
Zhuoran Wang | Oliver Lemon
Proceedings of the SIGDIAL 2013 Conference


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A Statistical Spoken Dialogue System using Complex User Goals and Value Directed Compression
Paul A. Crook | Zhuoran Wang | Xingkun Liu | Oliver Lemon
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics


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Improving the Confidence of Machine Translation Quality Estimates
Lucia Specia | Marco Turqui | Zhuoran Wang | John Shawe-Taylor | Craig Saunders
Proceedings of Machine Translation Summit XII: Papers

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Sentence-level confidence estimation for MT
Lucia Specia | Nicola Cancedda | Marc Dymetman | Craig Saunders | Marco Turchi | Nello Cristianini | Zhuoran Wang | John Shawe-Taylor
Proceedings of the 13th Annual conference of the European Association for Machine Translation


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Kernel Regression Framework for Machine Translation: UCL System Description for WMT 2008 Shared Translation Task
Zhuoran Wang | John Shawe-Taylor
Proceedings of the Third Workshop on Statistical Machine Translation


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Kernel Regression Based Machine Translation
Zhuoran Wang | John Shawe-Taylor | Sandor Szedmak
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers