229 citations · 478 across the 21 of their papers we have counts for
14 papers · 1 filter
Learning to Rank in Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative…
Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must b…
Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation
Peiqin Lin, Jiashuo Wang, Hinrich Schütze +1
The task of empathetic response generation aims to understand what feelings a speaker expresses on his/her experiences and then reply to the speaker appropriately. To solve the tas…
Addressing Token Uniformity in Transformers via Singular Value Transformation
Hanqi Yan, Lin Gui, Wenjie Li +1
Token uniformity is commonly observed in transformer-based models, in which different tokens share a large proportion of similar information after going through stacked multiple se…
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
Wenge Liu, Yi Cheng, Hao Wang +6
With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches tha…
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization
Shichao Sun, Wenjie Li
Encoder-decoder models have achieved remarkable success in abstractive text summarization, which aims to compress one or more documents into a shorter version without the loss of t…
Data Distillation for Text Classification
Yongqi Li, Wenjie Li
Deep learning techniques have achieved great success in many fields, while at the same time deep learning models are getting more complex and expensive to compute. It severely hind…
A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering
Yongqi Li, Wenjie Li, Liqiang Nie
In recent years, conversational agents have provided a natural and convenient access to useful information in people's daily life, along with a broad and new research topic, conver…
Incremental Knowledge Based Question Answering
Yongqi Li, Wenjie Li, Liqiang Nie
In the past years, Knowledge-Based Question Answering (KBQA), which aims to answer natural language questions using facts in a knowledge base, has been well developed. Existing app…
Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech
Wenjie Li, Benlai Tang, Xiang Yin +6
Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre. In this paper, we propose approaches to improving…
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization
Hai Ye, Wenjie Li, Lu Wang
Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL descript…
Incorporating Relevant Knowledge in Context Modeling and Response Generation
Yanran Li, Wenjie Li, Ziqiang Cao +1
To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge. Equipped with a knowledge base, chatbots are able to extract conversation-rela…
Meta-path Augmented Response Generation
Yanran Li, Wenjie Li
We propose a chatbot, namely Mocha to make good use of relevant entities when generating responses. Augmented with meta-path information, Mocha is able to mention proper entities f…
A Conditional Variational Framework for Dialog Generation
Xiaoyu Shen, Hui Su, Yanran Li +5
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to…