Ask Me Anything in Your Native Language

Nikita Sorokin, Dmitry Abulkhanov, Irina Piontkovskaya, Valentin Malykh


Abstract
Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for query and passage for retrieval from multi-lingual collection, together with cross-lingual generative reader. It achieves a new state of the art in both retrieval and end-to-end tasks on the XOR TyDi dataset outperforming the previous results up to 10% on several languages. We find that our approach can be generalized to more than 20 languages in zero-shot approach and outperform all previous models by 12%.
Anthology ID:
2022.naacl-main.30
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
395–406
Language:
URL:
https://aclanthology.org/2022.naacl-main.30
DOI:
10.18653/v1/2022.naacl-main.30
Bibkey:
Cite (ACL):
Nikita Sorokin, Dmitry Abulkhanov, Irina Piontkovskaya, and Valentin Malykh. 2022. Ask Me Anything in Your Native Language. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 395–406, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Ask Me Anything in Your Native Language (Sorokin et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.30.pdf
Data
MKQANatural QuestionsSQuADTriviaQA