6 citations · 16 across the 15 of their papers we have counts for
13 papers · 1 filter
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation
Yiwei Li, Fei Mi, Yitong Li +4
Stochastic sampling strategies such as top-k and top-p have been widely used in dialogue generation task. However, as an open-domain chatting system, there will be two different co…
EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset
Hang Yin, Pinren Lu, Ziang Li +2
The need for high-quality data has been a key issue hindering the research of dialogue tasks. Recent studies try to build datasets through manual, web crawling, and large pre-train…
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Yiwei Li, Shaoxiong Feng, Bin Sun +1
With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-…
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…
Hierarchical Inductive Transfer for Continual Dialogue Learning
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Pre-trained models have achieved excellent performance on the dialogue task. However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models fo…
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…
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillat…
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Shaoxiong Feng, Xuancheng Ren, Hongshen Chen +3
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more m…
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Shaoxiong Feng, Hongshen Chen, Kan Li +1
Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximu…