Using natural language prompts for machine translation

By Xavier García, Orhan Firat. 2022

38 citations recorded by the source corpus.

We explore the use of natural language prompts for controlling various aspects of the outputs generated by machine translation models. We demonstrate that natural language prompts allow us to influence properties like formality or specific dialect of the output. We show that using language names to control the output language of multilingual translation models enables positive transfer for unseen language pairs. This unlocks the ability to translate into languages not seen during fine-tuning by using their English names. We investigate how scale, number of pre-training steps, number of languages in fine-tuning, and language similarity affect this phenomenon.

Subfields: Prompting & Reasoning, Machine Translation

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Citations in the corpus

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022, methodology): “Prompt tuning has been applied to various applications for NLP including continual learning [59], model robustness [60, 61], summarization [62], machine translation [63], co-training [64], probing language models [65, 65], inverse prompting [66] and transfer learning [67].”
Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM (2023, unknown): “More-over, prompting-based MT offers the prospect of better control of outputs, e.g. in terms of quality, style and dialect (Garcia and Firat, 2022).”
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation (2022, background): “, 2020), and more recent work applying it to region-aware MT (Garcia and Firat, 2022).”