Search Results for author: Baoxun Wang

Found 18 papers, 1 papers with code

Guiding Variational Response Generator to Exploit Persona

no code implementations ACL 2020 Bowen Wu, Mengyuan Li, Zongsheng Wang, Yifu Chen, Derek Wong, Qihang Feng, Junhong Huang, Baoxun Wang

Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.

Response Generation

Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior

no code implementations WS 2019 Bowen Wu, Haoyang Huang, Zongsheng Wang, Qihang Feng, Jingsong Yu, Baoxun Wang

Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples.

Machine Reading Comprehension Sentence

LocalGAN: Modeling Local Distributions for Adversarial Response Generation

no code implementations25 Sep 2019 Zhen Xu, Baoxun Wang, huan zhang, Kexin Qiu, Deyuan Zhang, Chengjie Sun

This paper presents a new methodology for modeling the local semantic distribution of responses to a given query in the human-conversation corpus, and on this basis, explores a specified adversarial learning mechanism for training Neural Response Generation (NRG) models to build conversational agents.

Response Generation

LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics

no code implementations NAACL 2018 Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang

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.

Machine Translation Response Generation

Group Linguistic Bias Aware Neural Response Generation

no code implementations WS 2017 Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, Baoxun Wang

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.

Decoder Response Generation

Neural Response Generation via GAN with an Approximate Embedding Layer

no code implementations EMNLP 2017 Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, Chao Qi

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.

Generative Adversarial Network Machine Translation +1

Ranking Responses Oriented to Conversational Relevance in Chat-bots

no code implementations COLING 2016 Bowen Wu, Baoxun Wang, Hui Xue

For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only.


Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM

1 code implementation17 May 2016 Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang

Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability.

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