Cross-Language Transformer Adaptation for Frequently Asked Questions
Luca Di Liello, Daniele Bonadiman, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Alessandro Moschitti · Accademia University Press eBooks · 2020
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 well-known English Quora dataset, while the target domain 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