84 citations · 159 across the 13 of their papers we have counts for
13 papers
SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
Danlong Yuan, Wei Wu, Enhan Zhao +4
Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation. At…
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions
Yuxuan Wang, Zilong Zheng, Xueliang Zhao +3
Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and d…
Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving
Xueliang Zhao, Wenda Li, Lingpeng Kong
Large language models~(LLMs) present an intriguing avenue of exploration in the domain of formal theorem proving. Nonetheless, the full utilization of these models, particularly in…
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure
Xueliang Zhao, Lemao Liu, Tingchen Fu +3
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downst…
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation
Xueliang Zhao, Yuxuan Wang, Chongyang Tao +2
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) th…
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Xueliang Zhao, Tingchen Fu, Chongyang Tao +1
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…
Learning to Express in Knowledge-Grounded Conversation
Xueliang Zhao, Tingchen Fu, Chongyang Tao +3
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studie…
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Tingchen Fu, Xueliang Zhao, Chongyang Tao +2
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous…
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
Xueliang Zhao, Wei Wu, Can Xu +3
We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping respo…
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
Ruijian Xu, Chongyang Tao, Daxin Jiang +3
Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on buildi…
Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li, Can Xu, Wei Wu +3
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often r…
Low-Resource Knowledge-Grounded Dialogue Generation
Xueliang Zhao, Wei Wu, Chongyang Tao +3
Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning su…
A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
Xueliang Zhao, Chongyang Tao, Wei Wu +3
We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system. The challenges of building such a mod…