2018
EMNLP
EMNLP 2018
ITER: Improving Translation Edit Rate through Optimizable Edit Costs
Abstract
AbstractThe paper presents our participation in the WMT 2018 Metrics Shared Task. We propose an improved version of Translation Edit/Error Rate (TER). In addition to including the basic edit operations in TER, namely - insertion, deletion, substitution and shift, our metric also allows stem matching, optimizable edit costs and better normalization so as to correlate better with human judgement scores. The proposed metric shows much higher correlation with human judgments than TER.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization and Natural Language Processing
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Trend Setter
— Text Classification
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Keyword Pioneer
— metric optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Optimization & Theory > Optimization
Natural Language Processing > Applications > Machine Translation
Mathematics & Optimization > Optimization > Optimization
Machine Learning > Application Areas > Text Classification