Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering

• URL: http://arxiv.org/abs/2004.03101v2
• Date: Fri, 17 Apr 2020 06:46:36 GMT
• Title: Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering
• Authors: Pratyay Banerjee and Chitta Baral
• Abstract summary: Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning. We learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We propose a "knowledge fusion model" which leverages knowledge in BERT-based language models with externally retrieved knowledge.
• Score: 33.920269584939334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recently introduced open domain question answering challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts and compose facts to correctly answer questions. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We further propose a "knowledge fusion model" which leverages knowledge in BERT-based language models with externally retrieved knowledge and improves the knowledge understanding of the BERT-based language models. On both OpenBookQA and QASC datasets, the knowledge fusion model with semantically re-ranked knowledge outperforms previous attempts.

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