@inproceedings{ma2022open-domain,
author = {Ma, Kaixin and Cheng, Hao and Liu, Xiaodong and Nyberg, Eric and Gao, Jianfeng},
title = {Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge},
booktitle = {EMNLP 2022},
year = {2022},
month = {October},
abstract = {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).},
url = {http://approjects.co.za/?big=en-us/research/publication/open-domain-question-answering-via-chain-of-reasoning-over-heterogeneous-knowledge/},
}