DICTDIS: Dictionary Constrained Disambiguation for Improved NMT
Ayush Maheshwari, Preethi Jyothi, Ganesh Ramakrishnan

TL;DR
DictDis is a novel NMT system that effectively disambiguates multiple dictionary-based translation candidates, improving lexical constraints and translation quality across various domains and languages.
Contribution
It introduces a training method that incorporates multiple dictionary candidates to enhance disambiguation in lexically constrained NMT systems.
Findings
Superior disambiguation performance across domains
Improved BLEU scores by 2-3 points on some datasets
Effective handling of multiple candidate translations
Abstract
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 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…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Multimodal Machine Learning Applications
MethodsTest
