Search Results for author: Chengjie Sun

Found 14 papers, 2 papers with code

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

no code implementations31 Dec 2020 Shaobo Li, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Chengjie Sun, Zhenzhou Ji, Bingquan Liu

In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.

Document Embedding Open-Domain Question Answering

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.

ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention

no code implementations SEMEVAL 2018 Wenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu

Semantic Evaluation (SemEval) 2018 Task 12 {``}The Argument Reasoning Comprehension{''} committed to research natural language reasoning.

Information Retrieval Machine Translation +4

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.

Machine Translation

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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