2020
ACL
ACL 2020
Modeling Word Formation in English–German Neural Machine Translation
Abstract
AbstractThis paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology. Our linguistically sound segmentation is combined with a method for target-side inflection to accommodate modeling word formation. The best system variants employ source-side morphological analysis and model complex target-side words, improving over a standard system.
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Interdisciplinary Bridge
- Artificial Intelligence and Interdisciplinary and Natural Language Processing
Authors
Topics
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Resources & Methods > Text Representation
Interdisciplinary > Linguistics > Morphology
Machine Learning > Learning Types > Deep Learning
Deep Learning > Learning Types > Representation Learning