Search Results for author: Zhenxin Fu

Found 7 papers, 2 papers with code

ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation

no code implementations ACL 2022 Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan

To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.

Dialogue Generation Response Generation

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

1 code implementation7 Nov 2019 Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan

Information-seeking conversation system aims at satisfying the information needs of users through conversations.

Text Matching

Multilingual Dialogue Generation with Shared-Private Memory

no code implementations6 Oct 2019 Chen Chen, Lisong Qiu, Zhenxin Fu, Dongyan Zhao, Junfei Liu, Rui Yan

Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.

Cross-Lingual Transfer Dialogue Generation

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

no code implementations IJCNLP 2019 Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.

Sentence Style Transfer +1

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Natural Language Inference +2

Style Transfer in Text: Exploration and Evaluation

2 code implementations18 Nov 2017 Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan

Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.

Style Transfer Text Style Transfer

Cannot find the paper you are looking for? You can Submit a new open access paper.