2018.2. Unsupervised Neural Machine Translation 阅读笔记

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2018.2-Mikel Artetxe, Kyunghyum Cho-Unsupervised Nueral Machine Translation

UPV/EHU, New York University

ICLR2018

Abstract

• This paer
  • build upon the recent work on unsupervised embedding mappings
    • 这篇文献 和 这篇文献
  • a slightly modified attentional encoder-decoder model
    • combination of denoising and back-translation
  • novelty
    • Dual structure => handle both directions together
    • Shared encoder
    • fixed cross-lingual embeddings in the encoder during training
• Result
  • no parallel resource
    • WMT 2014 French -> English: 15.56 BLEU
    • WMT 2014 German -> English; 10.21 BLEU
  • combined with 100,000 parallel sentences
    • WMT 2014 French -> English: 21.81 BLEU
    • WMT 2014 German -> English; 15.24 BLEU
• Related Works
  • unsupervised cross-lingual embeddings
  • statistical deciperment for machine translation
  • low-resource neural machine translation

Method

System Architecture

• encoder: a two-layer bidirectional RNN (GRU cells with 600 hidden units)
• decoder: a two-layer RNN (GRU cells with 600 hidden units)
• dim. of the ebds.: 300
• attention mechanism: global attention method with the general alignment function (同这篇文献)

Unsupervised Training

• for each stc. in lang. L1, train allternating in 2 steps:
  • STEP 1: Denoising: shared encoder + L1 decoder
    • random swap
  • STEP 2: On-the-fly Backtranslation, including 2 parts
    • PART 1: translate in inference mode: shared encoder + L2 decoder
    • PART 2: for the translated stc.: shared encoder + L1 decoder

Expt.s

• Datasets:
  • Train: WMT 2014 French-English & German-English
  • Test: newstest2014, Tokenized BLEU (multi-bleu.perl script)
• Corpus Preprocessing:
  • tokenization and truecasing
  • byte pair encoding (BPE): manages to correctly translate rare words
  • learning on monolingual corpus:
    • 50,000 operations
    • Limite the vocabulary: most frequent 50,000 tokens.
    • Replace the rest with a special token .
    • Accelerate training: discarde sentences with more than 50 elements.
• Cross-lingual Embeddings:
  • word2vec, Mikolov et al., 2013: skip-gram
  • Map the embeddings to a shared space
• Training:
  • Cross-entopy loss func.
  • Train each system took about 4-5 days on a single Titan X GPU for the full unsupervised variant.
• Decoding:
  • Training time: greedy decoding
  • Test time: beam-search
• Result:
  

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