Dan Garrette
I am a Research Scientist based in New York. My research focuses on Natural Language Processing and Machine Learning, with an emphasis on weakly-supervised learning for low-resource languages. I completed my Ph.D. in Computer Science at The University of Texas at Austin in 2015 and was later a postdoc at the University of Washington. More information can be found at http://www.dhgarrette.com/.
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Character-Aware Models Improve Visual Text Rendering
Chitwan Saharia
William Chan
Sharan Narang
Irina Blok
RJ Mical
Mohammad Norouzi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (2023)
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder
Mihir Sanjay Kale
Shruti Rijhwani
Jean-Michel Sarr
Cindy Wang
John Wieting
Christo Kirov
Dana L. Dickinson
Bidisha Samanta
Connie Tao
David Adelani
Reeve Ingle
Dmitry Panteleev
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 1856-1884
Dialect-robust Evaluation of Generated Text
Jiao Sun
Elizabeth Clark
Tu Vu
Sebastian Gehrmann
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada (2023), pp. 6010-6028
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
Jan A. Botha
Xavier Garcia
Transactions of the Association for Computational Linguistics (2023)
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Understanding
Iulia Turc
John Wieting
Transactions of the Association for Computational Linguistics (2022)
Frequency Effects on Syntactic Rule Learning in Transformers
Jason Wei
Conference on Empirical Methods in Natural Language Processing (2021)
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
Eunsol Choi
Transactions of the Association for Computational Linguistics (2020)
