no code implementations • 2 Nov 2022 • Siqi Bao, Huang He, Jun Xu, Hua Lu, Fan Wang, Hua Wu, Han Zhou, Wenquan Wu, Zheng-Yu Niu, Haifeng Wang
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy.
no code implementations • 28 Jun 2022 • Han Zhou, Xinchao Xu, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Siqi Bao, Fan Wang, Haifeng Wang
Making chatbots world aware in a conversation like a human is a crucial challenge, where the world may contain dynamic knowledge and spatiotemporal state.
no code implementations • 22 Apr 2022 • Shihang Wang, Xinchao Xu, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang
In this task, the agent conducts empathetic responses along with the target of eliciting the user's positive emotions in the multi-turn dialog.
no code implementations • ACL 2022 • Zeming Liu, Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu
For example, users have determined the departure, the destination, and the travel time for booking a flight.
1 code implementation • Findings (ACL) 2022 • Xinchao Xu, Zhibin Gou, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang, Shihang Wang
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations.
3 code implementations • 20 Sep 2021 • Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.
1 code implementation • EMNLP 2021 • Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.
no code implementations • ACL 2021 • Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che
Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
no code implementations • 31 Dec 2020 • Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
no code implementations • ACL 2020 • Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.
2 code implementations • ACL 2020 • Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.
1 code implementation • IJCNLP 2019 • Zhibin Liu, Zheng-Yu Niu, Hua Wu, Haifeng Wang
Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation.
no code implementations • EMNLP 2017 • Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, Haifeng Wang
We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora.