Few-shot complex knowledge base question answering via meta reinforcement learning

Yuncheng Hua, Yuan-Fang Li, Reza Haffari, Guilin Qi, Tongtong Wu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types, harboring inherently different characteristics, e.g., difficulty level. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. Our method quickly and effectively adapts the meta-learned programmer to new questions based on the most similar questions retrieved from the training data. The meta-learned policy is then used to learn a good programming policy, utilizing the trial trajectories and their rewards for similar questions in the support set. Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and meta-training on tasks constructed from only 1% of the training set. We have released our code at https://github.com/DevinJake/MRL-CQA.

Original languageEnglish
Title of host publicationEMNLP 2020, 2020 Conference on Empirical Methods in Natural Language Processing
Subtitle of host publicationProceedings of the Conference
EditorsTrevor Cohn, Yulan He, Yang Liu
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages11
ISBN (Electronic)9781952148606
Publication statusPublished - 2020
EventEmpirical Methods in Natural Language Processing 2020 - Virtual, Punta Cana, Dominican Republic
Duration: 16 Nov 202020 Nov 2020
https://2020.emnlp.org/ (Website)
http://www.aclweb.org/anthology/volumes/2020.emnlp-main/ (Proceedings)


ConferenceEmpirical Methods in Natural Language Processing 2020
Abbreviated titleEMNLP 2020
Country/TerritoryDominican Republic
CityPunta Cana
Internet address

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