Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies

Marion Weller-Di Marco, Matthias Huck, Alexander Fraser

https://arxiv.org/abs/2203.13550

2022


Morphologically rich languages pose difficulties to machine
translation. Machine translation engines that rely on statistical
learning from parallel training data, such as state-of-the-art neural
systems, face challenges especially with rich morphology on the output
language side. Key challenges of rich target-side morphology in
data-driven machine translation include: (1) A large amount of
differently inflected word surface forms entails a larger vocabulary
and thus data sparsity. (2) Some inflected forms of infrequent terms
typically do not appear in the training corpus, which makes
closed-vocabulary systems unable to generate these unobserved
variants. (3) Linguistic agreement requires the system to correctly
match the grammatical categories between inflected word forms in the
output sentence, both in terms of target-side morpho-syntactic
wellformedness and semantic adequacy with respect to the input.

In this paper, we re-investigate two target-side linguistic processing
techniques: a lemma-tag strategy and a linguistically informed word
segmentation strategy. Our experiments are conducted on a
English-German translation task under three training corpus conditions
of different magnitudes. We find that a stronger Transformer baseline
leaves less room for improvement than a shallow-RNN encoder-decoder
model when translating in-domain. However, we find that linguistic
modeling of target-side morphology does benefit the Transformer model
when the same system is applied to out-of-domain input text. We also
successfully apply our approach to English to Czech translation.