  Domain-specific neural machine translation (NMT) systems (\eg, 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 word/phrase due to 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 constraint setting wherein 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 by implicitly aligning multiple candidate constraints. We
demonstrate the utility of \dictdis\ via extensive experiments on English-Hindi
and English-German sentences in a variety of domains including regulatory,
finance, engineering. We also present comparisons on standard benchmark test
datasets. In comparison with existing approaches for lexically constrained and
unconstrained NMT, we demonstrate superior performance with respect to
constraint copy and disambiguation related measures on all domains while also
obtaining improved fluency of up to 2-3 BLEU points on some domains.
