2020
EMNLP
EMNLP 2020
NUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task
Abstract
AbstractThe paper describes the Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously.Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— wals feature
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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 > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Neural Network Optimization
Deep Learning > Architectures > Transformers
Machine Learning > Learning Types > Multi-Task Learning
Natural Language Processing > Applications > Named Entity Recognition
Machine Learning > Learning Paradigms > Multi-Task Learning