Machine Translation into Low-resource Language Varieties
By Sachin Kumar, Antonios Anastasopoulos, Shuly Wintner, Yulia Tsvetkov. 2021
34 citations recorded by the source corpus.
State-of-the-art machine translation (MT) systems are typically trained to generate “standard” target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source–variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English–Russian MT system to generate Ukrainian and Belarusian, an English–Norwegian Bokmål system to generate Nynorsk, and an English–Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.
Subfields: Natural Language Processing
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In Natural Language Processing
- Yulia Tsvetkov
- Sachin Kumar
- Vidhisha Balachandran
- Antonios Anastasopoulos
- Lucille Njoo
- Anjalie Field
- Hinrich Schütze
- Mohit Bansal
Citations in the corpus
- Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, methodology): “, 2020) Exclusion Adapting for low-resource varieties (Chronopoulou et al., 2020; Kumar et al., 2021a)”