4 citations · 7 across the 7 of their papers we have counts for
7 papers · 1 filter
Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables
Bin Sun, Yitong Li, Fei Mi +3
Conditional variational models, using either continuous or discrete latent variables, are powerful for open-domain dialogue response generation. However, previous works show that c…
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder
Bin Sun, Shaoxiong Feng, Yiwei Li +4
Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings rem…
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning
Yiwei Li, Bin Sun, Shaoxiong Feng +1
There is a growing interest in improving the conversational ability of models by filtering the raw dialogue corpora. Previous filtering strategies usually rely on a scoring method…
Diversifying Neural Dialogue Generation via Negative Distillation
Yiwei Li, Shaoxiong Feng, Bin Sun +1
Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios. Recently, an interesting approach, namely negative tr…
Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Bin Sun, Shaoxiong Feng, Yiwei Li +2
Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the contex…
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
Bin Sun, Shaoxiong Feng, Yiwei Li +2
Many existing conversation models that are based on the encoder-decoder framework have focused on ways to make the encoder more complicated to enrich the context vectors so as to i…