no code implementations • 10 Sep 2018 • Ziwei Bai, Bo Yu, Bowen Wu, Zhuoran Wang, Baoxun Wang
Generating structured query language (SQL) from natural language is an emerging research topic.
no code implementations • COLING 2018 • Zongsheng Wang, Yunzhi Bai, Bowen Wu, Zhen Xu, Zhuoran Wang, Baoxun Wang
Generative dialog models usually adopt beam search as the inference method to generate responses.
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.
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.
no code implementations • IJCNLP 2017 • Xin Wang, Jianan Wang, Yuanchao Liu, Xiaolong Wang, Zhuoran Wang, Baoxun Wang
Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work.
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.
no code implementations • 5 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.