An Empirical Comparison of Question Classification Methods for Question Answering Systems

Eduardo Cortes, Vinicius Woloszyn, Arne Binder, Tilo Himmelsbach, Dante Barone, Sebastian Möller

Abstract
Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, “Who is the prime minister of the United Kingdom?” demands a name of a PERSON, while “When was the queen of the United Kingdom born?” entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitable for low-resourced languages.
Anthology ID:
2020.lrec-1.665
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5408–5416
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.665
DOI:
Bibkey:
Cite (ACL):
Eduardo Cortes, Vinicius Woloszyn, Arne Binder, Tilo Himmelsbach, Dante Barone, and Sebastian Möller. 2020. An Empirical Comparison of Question Classification Methods for Question Answering Systems. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5408–5416, Marseille, France. European Language Resources Association.
Cite (Informal):
An Empirical Comparison of Question Classification Methods for Question Answering Systems (Cortes et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.665.pdf
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