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Evaluating Neural Machine Translation Models for English-Japanese Task

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Association for Computational Linguistics
Association for Computational Linguistics

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Evaluating Neural Machine Translation in English-Japanese Task (TEAM ID: WEBLIO MT) Zhongyuan Zhu @raphaelshu 1
Empirically evaluate various models in EJ task ‣ Two network architectures 2 ‣ Three recurrent units ‣ LSTM, GRU, IRNN multi-layer encoder-decoder model soft-attention model ‣ Two kinds of training data ‣ naturally-ordered, pre-reordered
Results: perplexities 3
Results: evaluation scores 4 BLEU RIBES HUMAN JPO Baseline phrase-based SMT 29.80 0.691 Baseline hierarchical phrase-based SMT 32.56 0.746 Baseline Tree-to-string SMT 33.44 0.758 30.00 Submitted system 1 (NMT) 34.19 0.802 43.50 Submitted system 2 (NMT + System combination) 36.21 0.809 53.75 3.81 Best competitor 1: NAIST (Travatar System with NeuralMT Reranking) 38.17 0.813 62.25 4.04 Best competitor 2: naver (SMT t2s + Spell correction + NMT reranking) 36.14 0.803 53.25 4.00
Finding & Insights ‣ Soft-attention models outperforms multi-layer encoder-decoder models ‣ Training models on pre-reordered data hurts the performance ‣ NMT models tend to make grammatically valid but incomplete translations 5
Thanks. 6

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Evaluating Neural Machine Translation Models for English-Japanese Task

  • 1. Evaluating Neural Machine Translation in English-Japanese Task (TEAM ID: WEBLIO MT) Zhongyuan Zhu @raphaelshu 1
  • 2. Empirically evaluate various models in EJ task ‣ Two network architectures 2 ‣ Three recurrent units ‣ LSTM, GRU, IRNN multi-layer encoder-decoder model soft-attention model ‣ Two kinds of training data ‣ naturally-ordered, pre-reordered
  • 4. Results: evaluation scores 4 BLEU RIBES HUMAN JPO Baseline phrase-based SMT 29.80 0.691 Baseline hierarchical phrase-based SMT 32.56 0.746 Baseline Tree-to-string SMT 33.44 0.758 30.00 Submitted system 1 (NMT) 34.19 0.802 43.50 Submitted system 2 (NMT + System combination) 36.21 0.809 53.75 3.81 Best competitor 1: NAIST (Travatar System with NeuralMT Reranking) 38.17 0.813 62.25 4.04 Best competitor 2: naver (SMT t2s + Spell correction + NMT reranking) 36.14 0.803 53.25 4.00
  • 5. Finding & Insights ‣ Soft-attention models outperforms multi-layer encoder-decoder models ‣ Training models on pre-reordered data hurts the performance ‣ NMT models tend to make grammatically valid but incomplete translations 5