ArXiv Preprint
Domain-specific neural machine translation (NMT) systems (e.g., in
educational applications) are socially significant with the potential to help
make information accessible to a diverse set of users in multilingual
societies. It is desirable that such NMT systems be lexically constrained and
draw from domain-specific dictionaries. Dictionaries could present multiple
candidate translations for a source words/phrases on account of the polysemous
nature of words. The onus is then on the NMT model to choose the contextually
most appropriate candidate. Prior work has largely ignored this problem and
focused on the single candidate setting where the target word or phrase is
replaced by a single constraint. In this work we present DICTDIS, a lexically
constrained NMT system that disambiguates between multiple candidate
translations derived from dictionaries. We achieve this by augmenting training
data with multiple dictionary candidates to actively encourage disambiguation
during training. We demonstrate the utility of DICTDIS via extensive
experiments on English-Hindi sentences in a variety of domains including news,
finance, medicine and engineering. We obtain superior disambiguation
performance on all domains with improved fluency in some domains of up to 4
BLEU points, when compared with existing approaches for lexically constrained
and unconstrained NMT.
Ayush Maheshwari, Piyush Sharma, Preethi Jyothi, Ganesh Ramakrishnan
2022-10-13
