activity
20192022
most citedRetrieving Sequential Information for Non-Autoregressive Neural Machine Translation

11 citations · 36 across the 13 of their papers we have counts for

collaborators

14 papers

cs.CL2026

A New Role for Relevance: Guiding Corpus Interaction in Agentic Search

Jiangnan Li, Yuqing Li, Mo Yu +2

Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top- content, but document r…

cs.CL20221 cited

Mental Health Assessment for the Chatbots

Yong Shan, Jinchao Zhang, Zekang Li +2

Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and…

cs.CL20215 cited

Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation

Lei Shen, Jinchao Zhang, Jiao Ou +2

Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generat…

cs.CL20212 cited

Challenging Instances are Worth Learning: Generating Valuable Negative Samples for Response Selection Training

Yao Qiu, Jinchao Zhang, Huiying Ren +1

Retrieval-based chatbot selects the appropriate response from candidates according to the context, which heavily depends on a response selection module. A response selection module…

cs.CL2021

Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder

Yao Qiu, Jinchao Zhang, Jie Zhou

Recent work has proposed several efficient approaches for generating gradient-based adversarial perturbations on embeddings and proved that the model's performance and robustness c…

cs.CL2021

Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks

Yao Qiu, Jinchao Zhang, Jie Zhou

Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Pr…