Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, Hal Daumé III
Abstract
Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge and unstructured text, assuming text corpora is semi-structured. Building on dense retrieval methods, we propose a new multi-step retrieval approach (BeamDR) that iteratively forms an evidence chain through beam search in dense representations. When evaluated on multi-hop question answering, BeamDR is competitive to state-of-the-art systems, without using any semi-structured information. Through query composition in dense space, BeamDR captures the implicit relationships between evidence in the reasoning chain. The code is available at https://github.com/henryzhao5852/BeamDR.
BibTeX
@inproceedings{zhao-etal-2021-multi-step,
title = "Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval",
author = "Zhao, Chen and
Xiong, Chenyan and
Boyd-Graber, Jordan and
Daum{\'e} III, Hal",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.368/",
doi = "10.18653/v1/2021.naacl-main.368",
pages = "4635--4641"
}