Cross-Language Transformer Adaptation for Frequently Asked Questions

Luca Di Liello, Daniele Bonadiman, Alessandro Moschitti, Cristina Giannone, Andrea Favalli, Raniero Romagnoli
2020 Italian Conference on Computational Linguistics  
Transfer learning has been proven to be effective, especially when data for the target domain/task is scarce. Sometimes data for a similar task is only available in another language because it may be very specific. In this paper, we explore the use of machine-translated data to transfer models on a related domain. Specifically, we transfer models from the question duplication task (QDT) to similar FAQ selection tasks. The source domain is the wellknown English Quora dataset, while the target
more » ... ain is a collection of small Italian datasets for real case scenarios consisting of FAQ groups retrieved by pivoting on common answers. Our results show great improvements in the zero-shot learning setting and modest improvements using the standard transfer approach for direct in-domain adaptation 1 .
dblp:conf/clic-it/LielloBMGFR20 fatcat:lciwyucqazfq7i3jzxwh6uzgjy