Retrieve, program, repeat: complex knowledge base question answering via alternate meta-learning

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

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


A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages8
ISBN (Electronic)9780999241165
Publication statusPublished - 2020
EventInternational Joint Conference on Artificial Intelligence 2020 - Yokohama, Japan
Duration: 1 Jan 20213 Jan 2021
Conference number: 29th (Proceedings) (Website)


ConferenceInternational Joint Conference on Artificial Intelligence 2020
Abbreviated titleIJCAI 2020
Internet address


  • Natural Language Processing
  • Question Answering

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