Search Results for author: Paul Crook

Found 10 papers, 5 papers with code

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

1 code implementation10 Sep 2021 Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.

Dialogue State Tracking Question Answering +1

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

no code implementations10 May 2021 Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.

Dialogue State Tracking Transfer Learning

Continual Learning in Task-Oriented Dialogue Systems

no code implementations31 Dec 2020 Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Zhiguang Wang

Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.

Continual Learning Multi-Task Learning +2

Adding Chit-Chat to Enhance Task-Oriented Dialogues

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.

Dialogue Generation Dialogue Understanding +1

Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity

1 code implementation EMNLP 2020 Pedro Rodriguez, Paul Crook, Seungwhan Moon, Zhiguang Wang

Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know.

Information Seeking

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

1 code implementation IJCNLP 2019 Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston

These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.

Recommendation Systems

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager

1 code implementation3 Dec 2016 Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance.

Natural Language Understanding

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