Towards Neural Phrase-based Machine Translation

Code

• github.com/posenhuang/NPMT
  OfficialIn papertorch★ 0
• github.com/Microsoft/NPMT
  torch★ 0
• github.com/ykrmm/ICLR_2020
  pytorch★ 0
• github.com/ykrmm/TREMBA
  pytorch★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions