Translation Errors Significantly Impact Low-Resource Languages in
  Cross-Lingual Learning
Ashish Sunil Agrawal Barah Fazili Preethi Jyothi
Popular benchmarks (e.g., XNLI) used to evaluate cross-lingual language
understanding consist of parallel versions of English evaluation sets in
multiple target languages created with the help of professional translators.
When creating such parallel data, it is critical to ensure high-quality
translations for all target languages for an accurate characterization of
cross-lingual transfer. In this work, we find that translation inconsistencies
do exist and interestingly they disproportionally impact low-resource languages
in XNLI. To identify such inconsistencies, we propose measuring the gap in
performance between zero-shot evaluations on the human-translated and
machine-translated target text across multiple target languages; relatively
large gaps are indicative of translation errors. We also corroborate that
translation errors exist for two target languages, namely Hindi and Urdu, by
doing a manual reannotation of human-translated test instances in these two
languages and finding poor agreement with the original English labels these
instances were supposed to inherit.