We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is the introduction of the intermediary modules into the current retriever-reader pipeline. Unlike previous methods that solely rely on the retriever for gathering all evidence in isolation, our intermediary performs a chain of reasoning over the retrieved set. Specifically, our method links the retrieved evidence with its related global context into graphs and organizes them into a candidate list of evidence chains. Built upon pretrained language models, our system achieves competitive performance on two ODQA datasets, OTT-QA and NQ, against tables and passages from Wikipedia. In particular, our model substantially outperforms the previous state-of-the-art on OTT-QA with an exact match score of 47.3 (45 % relative gain).
@article{arxiv.2210.12338,
title = {Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge},
author = {Kaixin Ma and Hao Cheng and Xiaodong Liu and Eric Nyberg and Jianfeng Gao},
journal= {arXiv preprint arXiv:2210.12338},
year = {2022}
}