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Computer Science > Computation and Language

arXiv:2310.07008 (cs)
[Submitted on 10 Oct 2023]

Title:Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs

Authors:Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski, Anton Razzhigaev, Valentin Malykh, Alexander Panchenko
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Abstract:Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the models is limited and the quality decreases for questions with less popular entities. In this paper, we present a novel approach which works on top of the pre-trained Text-to-Text QA system to address this issue. Our simple yet effective method performs filtering and re-ranking of generated candidates based on their types derived from Wikidata "instance_of" property.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2310.07008 [cs.CL]
  (or arXiv:2310.07008v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2310.07008

Submission history

From: Anton Razzhigaev [view email]
[v1] Tue, 10 Oct 2023 20:49:43 UTC (8,186 KB)
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