Learning Open Domain Multi-hop Search Using Reinforcement Learning
Abstract
We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. The method learns a policy for directing existing information retrieval and machine reading resources to focus on relevant regions of a corpus. The approach formulates the learning problem as a Markov decision process with a state representation that encodes the dynamics of the search process and a reward structure that minimizes the number of documents that must be processed while still finding multi-hop paths. We implement the method in an actor-critic reinforcement learning algorithm and evaluate it on a dataset of search problems derived from a subset of English Wikipedia. The algorithm finds a family of policies that succeeds in extracting the desired information while processing fewer documents compared to several baseline heuristic algorithms.
Keywords
Cite
@article{arxiv.2205.15281,
title = {Learning Open Domain Multi-hop Search Using Reinforcement Learning},
author = {Enrique Noriega-Atala and Mihai Surdeanu and Clayton T. Morrison},
journal= {arXiv preprint arXiv:2205.15281},
year = {2022}
}
Comments
Accepted for publication at the Structured and Unstructured Knowledge Integration (SUKI) workshop, held at NAACL-HLT 2022