Papers › Towards Neural Phrase-based Machine Translation
Towards Neural Phrase-based Machine Translation
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng
In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our experiments show that NPMT achieves superior performances on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese machine translation tasks compared with strong NMT baselines. We also observe that our method produces meaningful phrases in output languages.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | IWSLT2014 German-English | Neural PBMT + LM [Huang2018] | BLEU score | 30.08 | #32 of 34 | Archive leaderboard | report |
| Machine Translation | IWSLT2015 English-German | NPMT + language model | BLEU score | 25.36 | #7 of 8 | Archive leaderboard | report |
| Machine Translation | IWSLT2015 German-English | NPMT + language model | BLEU score | 30.08 | #8 of 15 | Archive leaderboard | report |
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