113 citations · 164 across the 14 of their papers we have counts for
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cs.CL2022★ 2 cited
Dialect-robust Evaluation of Generated Text
Jiao Sun, Thibault Sellam, Elizabeth Clark +6
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producin…
cs.CL2022★ 1 cited
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
Tu Vu, Aditya Barua, Brian Lester +3
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case…
cs.CL2022
Leveraging QA Datasets to Improve Generative Data Augmentation
Dheeraj Mekala, Tu Vu, Timo Schick +1
The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation. In this work…