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- W4385574067 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." @default.
- W4385574067 created "2023-08-05" @default.
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- W4385574067 date "2022-01-01" @default.
- W4385574067 modified "2023-09-24" @default.
- W4385574067 title "Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment" @default.
- W4385574067 doi "https://doi.org/10.18653/v1/2022.emnlp-main.10" @default.
- W4385574067 hasPublicationYear "2022" @default.
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