Search Results for author: Zhuoran Wang

Found 11 papers, 0 papers with code

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.

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

On the Linear Belief Compression of POMDPs: A re-examination of current methods

no code implementations5 Aug 2015 Zhuoran Wang, Paul A. Crook, Wenshuo Tang, Oliver Lemon

Belief compression improves the tractability of large-scale partially observable Markov decision processes (POMDPs) by finding projections from high-dimensional belief space onto low-dimensional approximations, where solving to obtain action selection policies requires fewer computations.

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