1 code implementation • Findings (EMNLP) 2021 • Zhiyu Chen, Honglei Liu, Hu Xu, Seungwhan Moon, Hao Zhou, Bing Liu
As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions.
1 code implementation • NAACL 2021 • Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
no code implementations • COLING 2020 • Hu Xu, Seungwhan Moon, Honglei Liu, Pararth Shah, Bing Liu, Philip S. Yu
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.
no code implementations • 10 Apr 2020 • Honglei Liu, Yan Xu, Zhiqiang Zhang, Ni Wang, Yanqun Huang, Yanjun Hu, Zhenghan Yang, Rui Jiang, Hui Chen
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.
no code implementations • ICLR 2020 • Duc Bui, Kshitiz Malik, Jack Goetz, Honglei Liu, Seungwhan Moon, Anuj Kumar, Kang G. Shin
Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.
no code implementations • 27 Sep 2019 • Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, Anuj Kumar
To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency.
1 code implementation • ACL 2019 • Zhiyu Chen, Hanwen Zha, Honglei Liu, Wenhu Chen, Xifeng Yan, Yu Su
Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks.
Ranked #142 on Action Classification on Kinetics-400
no code implementations • 1 Dec 2018 • Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin
This may require constant exploration of various options that the system may have for the user and obtaining signals of user preferences on those.
2 code implementations • NAACL 2018 • Yu Su, Honglei Liu, Semih Yavuz, Izzeddin Gur, Huan Sun, Xifeng Yan
We study the problem of textual relation embedding with distant supervision.