Task: MRC
Model description
An XLM-RoBERTa Large reading comprehension model trained from the combination of TyDi and NQ datasets, starting from a fine-tuned Tydi xlm-roberta-large model.
Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model.
Usage
You can use this model directly with the PrimeQA pipeline for reading comprehension squad.ipynb.
BibTeX entry and citation info
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
@article{clark-etal-2020-tydi,
title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
author = "Clark, Jonathan H. and
Choi, Eunsol and
Collins, Michael and
Garrette, Dan and
Kwiatkowski, Tom and
Nikolaev, Vitaly and
Palomaki, Jennimaria",
journal = "Transactions of the Association for Computational Linguistics",
volume = "8",
year = "2020",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2020.tacl-1.30",
doi = "10.1162/tacl_a_00317",
pages = "454--470",
}
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