2021
EMNLP
EMNLP 2021
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
Abstract
AbstractIn this paper, we introduce the Eval4NLP-2021 shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but also to explain this score by identifying the words that negatively impact translation quality. We present the data, annotation guidelines and evaluation setup of the shared task, describe the six participating systems, and analyze the results. To the best of our knowledge, this is the first shared task on explainable NLP evaluation metrics. Datasets and results are available at https://github.com/eval4nlp/SharedTask2021.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— explainable evaluation
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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, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Resources & Methods > Transfer Learning
Machine Learning > Learning Types > Evaluation
Natural Language Processing > Applications > Quality Estimation