Readers versus Re-rankers para la Búsqueda de Respuestas sobre COVID-19 en literatura científica

  1. Peñas Padilla, Anselmo
  2. Lozano-Álvarez, Borja
  3. Berná, Javier
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2022

Número: 68

Páginas: 133-142

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

In this work we present a comparison between the two most used neural Question Answering (QA) architectures to solve the problem of information overload on COVID-19 related articles. The span extraction (reader) and the re-ranker. We have found that there are no studies that compare these two methods even though they are so widely used. We also performed a search of the best hyperparameters for this task, and tried to conclude whether a model pre-trained with biomedical documents such as bioBERT outperforms a general domain model such as BERT. We found that the domain model is not clearly superior to the generalist one. We have studied also the number of answers to be extracted per context to obtain consistently good results. Finally, we conclude that although both approaches (readers and re-rankers) are very competitive, readers obtain systematically better results.

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Fuente de los datos: Dialnet