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Communication Dans Un Congrès Année : 2019

CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations

Résumé

Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient , however such kind of data only exists for English and Chinese at the moment. The aim of this study is the development of such resources for other languages by proposing to generate in a semi-automatic way questions from the semantic Frame analysis of large corpora. The collect of natural questions is reduced to a validation/test set. We applied this method on the French CALOR-FRAME corpus to develop the CALOR-QUEST resource presented in this paper.
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Dates et versions

hal-02317018 , version 1 (15-10-2019)

Identifiants

  • HAL Id : hal-02317018 , version 1

Citer

Frédéric Béchet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, et al.. CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations. MRQA: Machine Reading for Question Answering - Workshop at EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing, Nov 2019, Hong Kong, China. ⟨hal-02317018⟩
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