Simplify-Then-Translate: Automatic Preprocessing for Black-Box Translation


  • Sneha Mehta Virginia Tech
  • Bahareh Azarnoush Netflix Inc.
  • Boris Chen Netflix Inc.
  • Avneesh Saluja Netflix Inc.
  • Vinith Misra Netflix Inc.
  • Ballav Bihani Netflix Inc.
  • Ritwik Kumar Netflix Inc.



Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that “simplifies” the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.




How to Cite

Mehta, S., Azarnoush, B., Chen, B., Saluja, A., Misra, V., Bihani, B., & Kumar, R. (2020). Simplify-Then-Translate: Automatic Preprocessing for Black-Box Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8488-8495.



AAAI Technical Track: Natural Language Processing