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Alam, Fardina Fathmiul (2)
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The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (1)
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1
Towards More Equitable Question Answering Systems: How Much More Data Do You Need? ...
Debnath, Arnab
;
Rajabi, Navid
;
Alam, Fardina Fathmiul
;
Anastasopoulos, Antonios
. - : arXiv, 2021
Abstract:
Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project, we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems. Code and data for reproducing our experiments are available here: https://github.com/NavidRajabi/EMQA. ... : Accepted at ACL 2021 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.2105.14115
https://arxiv.org/abs/2105.14115
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Towards more equitable question answering systems: How much more data do you need? ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Alam, Fardina Fathmiul
;
Anastasopoulos, Antonios
. - : Underline Science Inc., 2021
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