22 citations · 87 across the 8 of their papers we have counts for
8 papers
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Yong Shan, Zekang Li, Jinchao Zhang +4
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have…
Diversifying Dialogue Generation with Non-Conversational Text
Hui Su, Xiaoyu Shen, Sanqiang Zhao +5
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utt…
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Zeyang Lei, Zekang Li, Jinchao Zhang +5
Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the…
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
Canxiang Yan, Jianhao Yan, Yangyin Xu +2
Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation. While effectively representing structured context, static knowledg…
Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog
Zekang Li, Zongjia Li, Jinchao Zhang +3
Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challeng…
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations
Zekang Li, Cheng Niu, Fandong Meng +3
Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in…
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
Hui Su, Xiaoyu Shen, Rongzhi Zhang +4
Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challe…
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Baoyu Jing, Chenwei Lu, Deqing Wang +2
Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the fe…