This paper studies the effects of word-level linguistic annotations in
under-resourced neural machine translation, for which there is incomplete
evidence in the literature. The study covers eight language pairs, different
training corpus sizes, two architectures, and three types of annotation: dummy
tags (with no linguistic information at all), part-of-speech tags, and
morpho-syntactic description tags, which consist of part of speech and
morphological features. These linguistic annotations are interleaved in the
input or output streams as a single tag placed before each word. In order to
measure the performance under each scenario, we use automatic evaluation
metrics and perform automatic error classification. Our experiments show that,
in general, source-language annotations are helpful and morpho-syntactic
descriptions outperform part of speech for some language pairs. On the
contrary, when words are annotated in the target language, part-of-speech tags
systematically outperform morpho-syntactic description tags in terms of
automatic evaluation metrics, even though the use of morpho-syntactic
description tags improves the grammaticality of the output. We provide a
detailed analysis of the reasons behind this result.

研究了词级语言标注对资源不足的神经机器翻译的影响，通过实验发现，当在源语言进行标注时，语法 - 语义描述标签在某些语言对中表现优于词性标签；但在目标语言进行标注时，词性标签在自动评估指标上始终优于语法 - 语义描述标签，尽管后者可改善输出的语法性。该研究提供了对此结果的详细分析。