Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

• URL: http://arxiv.org/abs/2010.15877v1
• Date: Thu, 29 Oct 2020 18:34:55 GMT
• Title: Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
• Authors: Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi and Tongtong Wu
• Abstract summary: Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB) The conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
• Score: 55.08037694027792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 metatraining on tasks constructed from only 1% of the training set. We have released our code at https://github.com/DevinJake/MRL-CQA.

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