Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval-Reference-Cited by-同舟云学术

Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval

Published:2022-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Mao Kelong¹,Dou Zhicheng¹,Qian Hongjin²

Affiliation:

1. Renmin University of China, Beijing, China

2. Renmin University of China & Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098, China Unicom Innovation Ecological Cooperation Plan, Beijing Academy of Artificial Intelligence(BAAI)

Major Innovation & Planning Interdisciplinary Platform for the ``Double-First Class' Initiative, Renmin University of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3531961

Reference46 articles.

1. Curriculum learning

2. Curriculum Meta-Learning for Next POI Recommendation

3. Research Frontiers in Information Retrieval

4. Jeffrey Dalton , Chenyan Xiong , and Jamie Callan . 2020 . CAsT 2020: The Conver- sational Assistance Track Overview .. In In Proceedings of TREC. Jeffrey Dalton, Chenyan Xiong, and Jamie Callan. 2020. CAsT 2020: The Conver- sational Assistance Track Overview.. In In Proceedings of TREC.

5. Jeffrey Dalton , Chenyan Xiong , and Jamie Callan . 2020 . TREC CAsT 2019: The conversational assistance track overview . In In Proceedings of TREC. Jeffrey Dalton, Chenyan Xiong, and Jamie Callan. 2020. TREC CAsT 2019: The conversational assistance track overview. In In Proceedings of TREC.

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