Bisazza, A.

Monz, C.

Tran, K.

English

Lexical sparsity is a major challenge for machine translation into morphologically rich languages. We address this problem by modeling sequences of fine-grained morphological tags in a bilingual context. To overcome the issue of ambiguous word analyses, we introduce soft tags, which are under-specified representations retaining all possible morphological attributes of a word. In order to learn distributed representations for the soft tags and their interactions we adopt a neural network approach. This approach allows for the combination of source and target side information to model a wide range of inflection phenomena. Our re-inflection experiments show a substantial increase in accuracy compared to a model trained on morphologically disambiguated data. Integrated into an SMT decoder and evaluated for English-Italian and English-Russian translation, our model yields improvements of up to 1.0 BLEU over a competitive baseline

International Migration, Integration and Social Cohesion online publications

A Distributed Inflection Model for Translating into Morphologically Rich Languages

UvA-DARE

https://pure.uva.nl/ws/files/2694354/168717_mtsummit15.pdf

A Distributed Inflection Model for Translating into Morphologically Rich Languages

Abstract

Similar works

Full text

Available Versions

International Migration, Integration and Social Cohesion online publications

UvA-DARE

International Migration, Integration and Social Cohesion online publications