553 citations
- Beihang UniversityCN66 papers
- Microsoft Research Asia (China)CN12 papers
- Chinese Academy of SciencesCN9 papers
- Tsinghua UniversityCN6 papers
- National Cheng Kung UniversityTW4 papers
- University of Chinese Academy of SciencesCN4 papers
- Institute of SoftwareCN3 papers
- Zhejiang UniversityCN3 papers
- CAS Key Laboratory of Quantum Information, USTCCN2 papers
- Nankai UniversityCN2 papers
- State Key Laboratory of Computer Science2 papers
- State Key Laboratory of Low-Dimensional Quantum Physics2 papers
9 papers · 1 filter
Low-Resource Response Generation with Template Prior
Ze Yang, Wei Wu, Jian Yang +2
We study open domain response generation with limited message-response pairs. The problem exists in real-world applications but is less explored by the existing work. Since the pai…
Neural Melody Composition from Lyrics
Hangbo Bao, Shaohan Huang, Furu Wei +5
In this paper, we study a novel task that learns to compose music from natural language. Given the lyrics as input, we propose a melody composition model that generates lyrics-cond…
Keyphrase Generation with Correlation Constraints
Jun Chen, Xiaoming Zhang, Yu Wu +2
In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting i…
Dictionary-Guided Editing Networks for Paraphrase Generation
Shaohan Huang, Yu Wu, Furu Wei +1
An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to e…
Response Generation by Context-aware Prototype Editing
Yu Wu, Furu Wei, Shaohan Huang +3
Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response gen…
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
Yu Wu, Wei Wu, Zhoujun Li +1
We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence archite…