2019
ACL
ACL 2019
En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects
Abstract
AbstractThis paper introduces the first attempt to investigate morphological segmentation on En-Ar bilingual word embeddings using bilingual word embeddings model without word alignment (BilBOWA). We investigate the effect of sentence length and embedding size on the learning process. Our experiment shows that using the D3 segmentation scheme improves the accuracy of learning bilingual word embeddings up to 10 percentage points compared to the ATB and D0 schemes in all different training settings.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— word alignment
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Embedding Learning
Natural Language Processing > Resources & Methods > Lexical Semantics
Natural Language Processing > Resources & Methods > Transfer Learning
Deep Learning > Learning Types > Representation Learning
Machine Learning > Learning Types > Multi-Lingual Learning