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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.suki-1.4</identifier>
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%0 Conference Proceedings
%T Learning Open Domain Multi-hop Search Using Reinforcement Learning
%A Noriega-Atala, Enrique
%A Surdeanu, Mihai
%A Morrison, Clayton
%Y Chen, Wenhu
%Y Chen, Xinyun
%Y Chen, Zhiyu
%Y Yao, Ziyu
%Y Yasunaga, Michihiro
%Y Yu, Tao
%Y Zhang, Rui
%S Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F noriega-atala-etal-2022-learning
%X 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.
%R 10.18653/v1/2022.suki-1.4
%U https://aclanthology.org/2022.suki-1.4/
%U https://doi.org/10.18653/v1/2022.suki-1.4
%P 26-35

Markdown (Informal)

[Learning Open Domain Multi-hop Search Using Reinforcement Learning](https://aclanthology.org/2022.suki-1.4/) (Noriega-Atala et al., SUKI 2022)

• Learning Open Domain Multi-hop Search Using Reinforcement Learning (Noriega-Atala et al., SUKI 2022)

ACL

• Enrique Noriega-Atala, Mihai Surdeanu, and Clayton Morrison. 2022. Learning Open Domain Multi-hop Search Using Reinforcement Learning. In Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI), pages 26–35, Seattle, USA. Association for Computational Linguistics.