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A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs.
Confidence weighting and test reliability
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Towards A Rigorous Science of Interpretable Machine Learning
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Development of machine translation technology for assisting health communication: A systematic review
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Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller. 2018 · 2018
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Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2018 · 2018
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Quality estimation for machine translation
Lucia Specia, Carolina Scarton, and Gustavo Henrique Paetzold. 2018 · 2018
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Editors’ foreword to the special issue on human factors in neural machine translation
Sheila Castilho, Federico Gaspari, Joss Moorkens, Maja Popović, and Antonio Toral. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Erick Fonseca, Lisa Yankovskaya, André F. T. Martins, Mark Fishel, and Christian Federmann. 2019 · 2019
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OpenKiwi: An open source framework for quality estimation
Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, and André F. T. Martins. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
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Accuracy of parent perception of comprehension of discharge instructions: role of plan complexity and health literacy
Alexander F Glick, Jonathan S Farkas, Rebecca E Rosenberg, Alan L Mendelsohn, Suzy Tomopoulos, Arthur H Fierman, Benard P Dreyer, Michael Migotsky, Jennifer Melgar, and H Shonna Yin. 2020 · 2020
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Backtranslation feedback improves user confidence in MT, not quality
Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, and Lisa Yankovskaya. 2021 · 2021
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Quality estimation via backtranslation at the wmt 2022 quality estimation task
Sweta Agrawal, Nikita Mehandru, Niloufar Salehi, and Marine Carpuat. 2022 · 2022
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Human-centered evaluation of explanations
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A dataset of simulated patient-physician medical interviews with a focus on respiratory cases
Faiha Fareez, Tishya Parikh, Christopher Wavell, Saba Shahab, Meghan Chevalier, Scott Good, Isabella De Blasi, Rafik Rhouma, Christopher McMahon, Jean-Paul Lam, et al. 2022 · 2022
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Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André F. T. Martins. 2022 · 2022
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Patient discharge instructions in the emergency department and their effects on comprehension and recall of discharge instructions: a systematic review and meta-analysis
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COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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COMET - deploying a new state-of-the-art MT evaluation metric in production
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
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Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos. 2021 · 2021
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The Flores-101 evaluation benchmark for low-resource and multilingual machine translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022 · 2022
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A Research Agenda for Using Machine Translation in Clinical Medicine
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Uncalibrated Models Can Improve Human-AI Collaboration
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