  Lexicon or dictionary generation across domains is of significant societal
importance, as it can potentially enhance information accessibility for a
diverse user base while preserving language identity. Prior work in the field
primarily focuses on bilingual lexical induction, which deals with word
alignments using mapping-based or corpora-based approaches. Though initiated by
researchers, the research associated with lexicon generation is limited, even
more so with domain-specific lexicons. This task becomes particularly important
in atypical medical, engineering, and other technical domains, owing to the
highly infrequent usage of the terms and negligibly low data availability of
technical terms in many low-resource languages. Owing to the research gap in
lexicon generation, especially with a limited focus on the domain-specific
area, we propose a new model to generate dictionary words for 6 Indian
languages in the multi-domain setting. Our model consists of domain-specific
and domain-generic layers that encode information, and these layers are invoked
via a learnable routing technique. Further, we propose an approach to
explicitly leverage the relatedness between these Indian languages toward
coherent translation. We also release a new benchmark dataset across 6 Indian
languages that span 8 diverse domains that can propel further research in
domain-specific lexicon induction. We conduct both zero-shot and few-shot
experiments across multiple domains to show the efficacy of our proposed model
in generalizing to unseen domains and unseen languages.
