Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge - Department of Natural Language Processing & Knowledge Discovery Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge

Philippe Jolivet
  • Fonction : Auteur
  • PersonId : 1082466
Alexandre Durand-Salmon
  • Fonction : Auteur
  • PersonId : 1082467

Résumé

A key bottleneck for developing dialog models is the lack of adequate training data. Due to privacy issues, dialog data is even scarcer in the health domain. We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data. We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model. We show that our data creation method has several advantages. Not only does it allow for the semi-automatic creation of large quantities of training data. It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions.
Fichier principal
Vignette du fichier
C20-Anna-healthbot.pdf (314.72 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03020294 , version 1 (23-11-2020)

Identifiants

  • HAL Id : hal-03020294 , version 1

Citer

Anna Liednikova, Philippe Jolivet, Alexandre Durand-Salmon, Claire Gardent. Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge. Proceedings of the 28th Conference on Computational Linguistics, Dec 2020, Barcelona, Spain. ⟨hal-03020294⟩
111 Consultations
121 Téléchargements

Partager

Gmail Facebook X LinkedIn More