@inproceedings{limkonchotiwat-etal-2024-mccrolin,
title = "{M}c{C}rolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Manakul, Potsawee and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.157/",
doi = "10.18653/v1/2024.findings-emnlp.157",
pages = "2780--2793",
abstract = "Automated question answering (QA) systems are increasingly relying on robust cross-lingual retrieval to identify and utilize information from multilingual sources, ensuring comprehensive and contextually accurate responses. Existing approaches often struggle with consistency across multiple languages and multi-size input scenarios. To address these challenges, we propose McCrolin, a Multi-consistency Cross-lingual training framework, leveraging multi-task learning to enhance cross-lingual consistency, ranking stability, and input-size robustness. Experimental results demonstrate that McCrolin achieves state-of-the-art performance on standard cross-lingual retrieval QA datasets. Furthermore, McCrolin outperforms competitors when dealing with various input sizes on downstream tasks. In terms of generalizability, results from further analysis show that our method is effective for various encoder architectures and sizes."
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<abstract>Automated question answering (QA) systems are increasingly relying on robust cross-lingual retrieval to identify and utilize information from multilingual sources, ensuring comprehensive and contextually accurate responses. Existing approaches often struggle with consistency across multiple languages and multi-size input scenarios. To address these challenges, we propose McCrolin, a Multi-consistency Cross-lingual training framework, leveraging multi-task learning to enhance cross-lingual consistency, ranking stability, and input-size robustness. Experimental results demonstrate that McCrolin achieves state-of-the-art performance on standard cross-lingual retrieval QA datasets. Furthermore, McCrolin outperforms competitors when dealing with various input sizes on downstream tasks. In terms of generalizability, results from further analysis show that our method is effective for various encoder architectures and sizes.</abstract>
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%0 Conference Proceedings
%T McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering
%A Limkonchotiwat, Peerat
%A Ponwitayarat, Wuttikorn
%A Lowphansirikul, Lalita
%A Manakul, Potsawee
%A Udomcharoenchaikit, Can
%A Chuangsuwanich, Ekapol
%A Nutanong, Sarana
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F limkonchotiwat-etal-2024-mccrolin
%X Automated question answering (QA) systems are increasingly relying on robust cross-lingual retrieval to identify and utilize information from multilingual sources, ensuring comprehensive and contextually accurate responses. Existing approaches often struggle with consistency across multiple languages and multi-size input scenarios. To address these challenges, we propose McCrolin, a Multi-consistency Cross-lingual training framework, leveraging multi-task learning to enhance cross-lingual consistency, ranking stability, and input-size robustness. Experimental results demonstrate that McCrolin achieves state-of-the-art performance on standard cross-lingual retrieval QA datasets. Furthermore, McCrolin outperforms competitors when dealing with various input sizes on downstream tasks. In terms of generalizability, results from further analysis show that our method is effective for various encoder architectures and sizes.
%R 10.18653/v1/2024.findings-emnlp.157
%U https://aclanthology.org/2024.findings-emnlp.157/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.157
%P 2780-2793
Markdown (Informal)
[McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering](https://aclanthology.org/2024.findings-emnlp.157/) (Limkonchotiwat et al., Findings 2024)
ACL
- Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, and Sarana Nutanong. 2024. McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 2780–2793, Miami, Florida, USA. Association for Computational Linguistics.