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Récupération de passages basée sur un graphe d'attention amélioré par des entités
1 : Laboratoire d'Informatique de Grenoble
Centre National de la Recherche Scientifique : UMR5217, Université Grenoble Alpes, Institut polytechnique de Grenoble - Grenoble Institute of Technology, Centre National de la Recherche Scientifique
2 : Schneider Electric Industries S.A.S.
* : Auteur correspondant
Schneider Electric Science and Technology
Passage retrieval is crucial in specialized domains where documents are long and complex, such as patents, legal documents, scientific reports, etc. We explore in this paper the integration of Entities and passages in Heterogeneous Attention Graph Models dedicated to passage retrieval. We use the two passage retrieval architectures based on re-ranking proposed in [1]. We experiment our proposal on the TREC CAR Y3 Passage Retrieval Task. The results obtained show an improvement over state-of-the-art techniques and proves the effectiveness of the approach. Our experiments also show the importance of using adequate parameters for such approach.
