Long Document Re-ranking with Modular Re-ranker

Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevanc...

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Veröffentlicht in:	arXiv.org 2022-06
Hauptverfasser:	Gao, Luyu, Callan, Jamie
Format:	Artikel
Sprache:	eng
Schlagworte:	Coders Computer Science - Computation and Language Computer Science - Information Retrieval Documents Modules Queries Ranking Representations Work breaks
Online-Zugang:	Volltext
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