Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation
Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez · 2020
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.