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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.477/ Abstract: Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constraint terms are provided as lemmas. In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. We explore linguistically motivated rule-based and data-driven neural-based inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
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URL: https://underline.io/lecture/37994-rule-based-morphological-inflection-improves-neural-terminology-translation https://dx.doi.org/10.48448/y0cj-my74
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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