Translation Artifacts in Cross-lingual Transfer Learning

Mikel Artetxe, Gorka Labaka, Eneko Agirre


Abstract
Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique. In this paper, we show that such translation process can introduce subtle artifacts that have a notable impact in existing cross-lingual models. For instance, in natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them, which current models are highly sensitive to. We show that some previous findings in cross-lingual transfer learning need to be reconsidered in the light of this phenomenon. Based on the gained insights, we also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.
Anthology ID:
2020.emnlp-main.618
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7674–7684
Language:
URL:
https://aclanthology.org/2020.emnlp-main.618
DOI:
10.18653/v1/2020.emnlp-main.618
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.618.pdf
Video:
 https://slideslive.com/38939034
Code
 artetxem/esxnli
Data
esXNLIMLQAMultiNLIParaCrawlSQuADXNLIXQuAD