In this paper, we propose a new task of machine translation (MT), which is
based on no parallel sentences but can refer to a ground-truth bilingual
dictionary. Motivated by the ability of a monolingual speaker learning to
translate via looking up the bilingual dictionary, we propose the task to see
how much potential an MT system can attain using the bilingual dictionary and
large scale monolingual corpora, while is independent on parallel sentences. We
propose anchored training (AT) to tackle the task. AT uses the bilingual
dictionary to establish anchoring points for closing the gap between source
language and target language. Experiments on various language pairs show that
our approaches are significantly better than various baselines, including
dictionary-based word-by-word translation, dictionary-supervised cross-lingual
word embedding transformation, and unsupervised MT. On distant language pairs
that are hard for unsupervised MT to perform well, AT performs remarkably
better, achieving performances comparable to supervised SMT trained on more
than 4M parallel sentences.

本文提出了一项基于双语词典而非平行句子的机器翻译新任务，采用 “锚定训练”(AT) 方法对其进行处理，实验证明该方法较其他基线方法表现更佳，尤其对于难以自我学习的语言对而言，其表现接近于受监督训练的神经机器翻译，并能发挥双语词典和大规模单语语料库的潜能。

基于双语词典的神经机器翻译，无需使用平行语料

Bilingual Dictionary Based Neural Machine Translation without Using  Parallel Sentences

We study several methods for full or partial sharing of the decoder
parameters of multilingual NMT models. We evaluate both fully supervised and
zero-shot translation performance in 110 unique translation directions using
only the WMT 2019 shared task parallel datasets for training. We use additional
test sets and re-purpose evaluation methods recently used for unsupervised MT
in order to evaluate zero-shot translation performance for language pairs where
no gold-standard parallel data is available. To our knowledge, this is the
largest evaluation of multi-lingual translation yet conducted in terms of the
total size of the training data we use, and in terms of the diversity of
zero-shot translation pairs we evaluate. We conduct an in-depth evaluation of
the translation performance of different models, highlighting the trade-offs
between methods of sharing decoder parameters. We find that models which have
task-specific decoder parameters outperform models where decoder parameters are
fully shared across all tasks.

本文研究了多语言 NMT 模型的解码器参数的完全或部分共享的几种方法，在仅使用 WMT2019 共享任务并行数据集进行训练的 110 个独特翻译方向上评估了完全监督和零样本翻译性能。通过使用其他测试集并重新利用最近用于无监督 MT 的评估方法来评估在没有黄金标准平行数据的语言对的零样本翻译性能。我们进行了不同模型翻译性能的深入评估，突出了解码器参数共享方法之间的权衡取舍。我们发现，在具有任务特定解码器参数的模型中，优于在所有任务上完全共享解码器参数的模型。