FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIR's WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
The abstract of the paper is the following:
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations. This system improves upon our WMT'18 submission by 4.5 BLEU points.
This model was contributed by stas. The original code can be found here.
- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
either. Its tokenizer is very similar to [
XLMTokenizer] and the main model is derived from [BartModel].
[[autodoc]] FSMTConfig
[[autodoc]] FSMTTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary
[[autodoc]] FSMTModel - forward
[[autodoc]] FSMTForConditionalGeneration - forward