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Kwabena Amponsah-Kaakyire
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2020 – today
- 2022
- [c4]Kwabena Amponsah-Kaakyire, Daria Pylypenko, Josef van Genabith, Cristina España-Bonet:
Explaining Translationese: why are Neural Classifiers Better and what do they Learn? BlackboxNLP@EMNLP 2022: 281-296 - [i3]Kwabena Amponsah-Kaakyire, Daria Pylypenko, Josef van Genabith, Cristina España-Bonet:
Explaining Translationese: why are Neural Classifiers Better and what do they Learn? CoRR abs/2210.13391 (2022) - 2021
- [c3]Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet:
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification. EMNLP (1) 2021: 8596-8611 - [c2]Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska
, Ondrej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussà, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri:
Findings of the 2021 Conference on Machine Translation (WMT21). WMT@EMNLP 2021: 1-88 - [i2]Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet:
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification. CoRR abs/2109.07604 (2021) - 2020
- [c1]Jesujoba O. Alabi, Kwabena Amponsah-Kaakyire, David Ifeoluwa Adelani, Cristina España-Bonet:
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi. LREC 2020: 2754-2762
2010 – 2019
- 2019
- [i1]Jesujoba O. Alabi, Kwabena Amponsah-Kaakyire, David Ifeoluwa Adelani, Cristina España-Bonet:
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi. CoRR abs/1912.02481 (2019)
Coauthor Index
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