Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment

• URL: http://arxiv.org/abs/2212.13036v1
• Date: Mon, 26 Dec 2022 08:12:41 GMT
• Title: Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment
• Authors: Yechun Tang, Xiaoxia Cheng, Weiming Lu
• Abstract summary: We introduce an alignment-enhanced complex question answering framework, called ALCQA. We train a question rewriting model to align the question and each action, and utilize a pretrained language model to implicitly align the question and KG artifacts. We retrieve top-k similar question-answer pairs at the inference stage through question-to-question alignment and propose a novel reward-guided action sequence selection strategy.
• Score: 6.646646618666681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex knowledge base question answering can be achieved by converting questions into sequences of predefined actions. However, there is a significant semantic and structural gap between natural language and action sequences, which makes this conversion difficult. In this paper, we introduce an alignment-enhanced complex question answering framework, called ALCQA, which mitigates this gap through question-to-action alignment and question-to-question alignment. We train a question rewriting model to align the question and each action, and utilize a pretrained language model to implicitly align the question and KG artifacts. Moreover, considering that similar questions correspond to similar action sequences, we retrieve top-k similar question-answer pairs at the inference stage through question-to-question alignment and propose a novel reward-guided action sequence selection strategy to select from candidate action sequences. We conduct experiments on CQA and WQSP datasets, and the results show that our approach outperforms state-of-the-art methods and obtains a 9.88\% improvements in the F1 metric on CQA dataset. Our source code is available at https://github.com/TTTTTTTTy/ALCQA.

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