2019
ACL
ACL 2019
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
Abstract
AbstractThis paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context. This task requires us to produce the lemma and morpho-syntactic description of each token in a sequence, for 107 treebanks. We approach this task with a hierarchical neural conditional random field (CRF) model which predicts each coarse-grained feature (eg. POS, Case, etc.) independently. However, most treebanks are under-resourced, thus making it challenging to train deep neural models for them. Hence, we propose a multi-lingual transfer training regime where we transfer from multiple related languages that share similar typology.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— neural tagging
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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, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Domain Adaptation
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Learning Paradigms > Transfer Learning
Natural Language Processing > Understanding > Morphology
Machine Learning > Learning Types > Multi-Lingual Learning
Natural Language Processing > Applications > Morphology