Search Results for author: Chengjie Sun

Found 17 papers, 2 papers with code

Pre-training Language Models with Deterministic Factual Knowledge

no code implementations20 Oct 2022 Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

Further experiments on question-answering datasets show that trying to learn a deterministic relationship with the proposed methods can also help other knowledge-intensive tasks.

Knowledge Probing Question Answering

How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

no code implementations Findings (ACL) 2022 Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.

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

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 Response Generation

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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