In spite of the recent success of neural machine translation (NMT) in
standard benchmarks, the lack of large parallel corpora poses a major practical
problem for many language pairs. There have been several proposals to alleviate
this issue with, for instance, triangulation and semi-supervised learning
techniques, but they still require a strong cross-lingual signal. In this work,
we completely remove the need of parallel data and propose a novel method to
train an NMT system in a completely unsupervised manner, relying on nothing but
monolingual corpora. Our model builds upon the recent work on unsupervised
embedding mappings, and consists of a slightly modified attentional
encoder-decoder model that can be trained on monolingual corpora alone using a
combination of denoising and backtranslation. Despite the simplicity of the
approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014
French-to-English and German-to-English translation. The model can also profit
from small parallel corpora, and attains 21.81 and 15.24 points when combined
with 100,000 parallel sentences, respectively. Our implementation is released
as an open source project.

本研究提出了一种全新的方法，在没有平行数据的情况下，只利用单语数据即可训练 NMT 系统。这种基于注意力机制的编码器解码器模型结合去噪和回译技术， 在 WMT 2014 的法英和德英翻译中获得了 15.56 和 10.21 BLEU 分数，且能够利用少量的平行数据来提高翻译质量。

无监督神经机器翻译

Unsupervised Neural Machine Translation

The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative sequences and their posterior probabilities in a compact
form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a
LatticeLSTM that is able to consume word lattices, and can be used as encoder
in an attentional encoder-decoder model. We integrate lattice posterior scores
into this architecture by extending the TreeLSTM's child-sum and forget gates
and introducing a bias term into the attention mechanism. We experiment with
speech translation lattices and report consistent improvements over baselines
that translate either the 1-best hypothesis or the lattice without posterior
scores.

研究使用基于树结构的神经网络模型作为编码器建立语音翻译模型，使用词汇图来代替输入，可以提高翻译准确性。