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Communication Dans Un Congrès Année : 2022

Passage Retrieval on Structured Documents using Graph Attention Networks

Résumé

Passage Retrieval systems aim at retrieving and ranking small text units according to their estimated relevance to a query. A usual practice is to consider the context a passage appears in (its containing document, neighbour passages, etc.) to improve its relevance estimation. In this work, we study the use of Graph Attention Networks (GATs), a graph node embedding method, to perform passage contextualization. More precisely, we first propose a document graph representation based on several inter- and intra-document relations. Then, we investigate two ways of leveraging the use of GATs on this representation in order to incorporate contextual information for passage retrieval. We evaluate our approach on a Passage Retrieval task for structured documents: CLEF-IP2013. Our results show that our document graph representation coupled with the expressive power of GATs allows for a better context representation leading to improved performances
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Dates et versions

hal-03626054 , version 1 (22-07-2022)

Identifiants

Citer

Lucas Albarede, Philippe Mulhem, Lorraine Goeuriot, Claude Le Pape-Gardeux, Sylvain Marié, et al.. Passage Retrieval on Structured Documents using Graph Attention Networks. 44th European Conference on IR Research (ECIR 2022), Apr 2022, Stavanger, Norway. pp.13-21, ⟨10.1007/978-3-030-99739-7_2⟩. ⟨hal-03626054⟩
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