5 citations · 12 across the 6 of their papers we have counts for
7 papers · 1 filter
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
Zhiliang Tian, Wei Bi, Dongkyu Lee +4
Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing…
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
Xiaocheng Feng, Yawei Sun, Bing Qin +5
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while alter…
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
Jian Wang, Junhao Liu, Wei Bi +4
Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aw…
Relevance-Promoting Language Model for Short-Text Conversation
Xin Li, Piji Li, Wei Bi +2
Despite the effectiveness of sequence-to-sequence framework on the task of Short-Text Conversation (STC), the issue of under-exploitation of training data (i.e., the supervision si…
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Yiping Song, Zequn Liu, Wei Bi +2
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…
Generating Multiple Diverse Responses for Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +2
Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the inpu…