GitLab

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/336

Differential Revision: D12876709

Pulled By: myleott

fbshipit-source-id: a31536e2eb93f752600b9940c28e9b9fcefc8b86

Summary:
We'd like to resue the noising functions and DenoisingDataset in
adversarial training. However, current noising functions assume the input are
subword tokens. The goal of this diff is to extend it so the noising can be
applied to word tokens. Since we're mostly interested in the word shuffle
noising, so I only modified the WordShuffle class.

Reviewed By: liezl200

Differential Revision: D10523177

fbshipit-source-id: 1e5d27362850675010e73cd38850c890d42652ab

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/306

This uses a source dataset to generate a batch of {source: noisy source, target: original clean source} which allows us to train a denoising autoencoding component as part of a seq2seq model.

Reviewed By: xianxl

Differential Revision: D10078981

fbshipit-source-id: 026225984d4a97062ac05dc3a36e79b5c841fe9c

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/305

Previously, noising code assumed that every sentence had an EOS which had to be excluded from noising operations (since we shouldn't drop, blank, or shuffle EOS). This logic allows the noising module to handle sentences with EOS and without EOS

Reviewed By: xianxl

Differential Revision: D10114425

fbshipit-source-id: 04ec8547343eb94266bda1ac7fca3d8a1991c9f4