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    <abstract>Answering open-domain questions through unsupervised methods poses challenges for both machine-reading (MR) and language model (LM) -based approaches. The MR-based approach suffers from sparsity issues in extracted knowledge graphs (KGs), while the performance of the LM-based approach significantly depends on the quality of the retrieved context for questions. In this paper, we compare these approaches and propose a novel methodology that leverages directional predicate entailment (inference) to address these limitations. We use entailment graphs (EGs), with natural language predicates as nodes and entailment as edges, to enhance parsed KGs by inferring unseen assertions, effectively mitigating the sparsity problem in the MR-based approach. We also show EGs improve context retrieval for the LM-based approach. Additionally, we present a Boolean QA task, demonstrating that EGs exhibit comparable directional inference capabilities to large language models (LLMs). Our results highlight the importance of inference in open-domain QA and the improvements brought by leveraging EGs.</abstract>
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%0 Conference Proceedings
%T Complementary Roles of Inference and Language Models in QA
%A Cheng, Liang
%A Hosseini, Mohammad Javad
%A Steedman, Mark
%Y Surdeanu, Mihai
%Y Riloff, Ellen
%Y Chiticariu, Laura
%Y Frietag, Dayne
%Y Hahn-Powell, Gus
%Y Morrison, Clayton T.
%Y Noriega-Atala, Enrique
%Y Sharp, Rebecca
%Y Valenzuela-Escarcega, Marco
%S Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cheng-etal-2023-complementary
%X Answering open-domain questions through unsupervised methods poses challenges for both machine-reading (MR) and language model (LM) -based approaches. The MR-based approach suffers from sparsity issues in extracted knowledge graphs (KGs), while the performance of the LM-based approach significantly depends on the quality of the retrieved context for questions. In this paper, we compare these approaches and propose a novel methodology that leverages directional predicate entailment (inference) to address these limitations. We use entailment graphs (EGs), with natural language predicates as nodes and entailment as edges, to enhance parsed KGs by inferring unseen assertions, effectively mitigating the sparsity problem in the MR-based approach. We also show EGs improve context retrieval for the LM-based approach. Additionally, we present a Boolean QA task, demonstrating that EGs exhibit comparable directional inference capabilities to large language models (LLMs). Our results highlight the importance of inference in open-domain QA and the improvements brought by leveraging EGs.
%R 10.18653/v1/2023.pandl-1.8
%U https://aclanthology.org/2023.pandl-1.8
%U https://doi.org/10.18653/v1/2023.pandl-1.8
%P 75-91

Markdown (Informal)

[Complementary Roles of Inference and Language Models in QA](https://aclanthology.org/2023.pandl-1.8) (Cheng et al., PANDL-WS 2023)

• Complementary Roles of Inference and Language Models in QA (Cheng et al., PANDL-WS 2023)

ACL

• Liang Cheng, Mohammad Javad Hosseini, and Mark Steedman. 2023. Complementary Roles of Inference and Language Models in QA. In Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning, pages 75–91, Singapore. Association for Computational Linguistics.