Abstract
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data scarcity issue in other languages. The lack of fact-checking datasets in low-resource languages calls for an effective cross-lingual transfer technique for fact-checking. Additionally, trustworthy information in different languages can be complementary and helpful in verifying facts. To this end, we present the first fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from multiple languages through a cross-lingual retriever. Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage. The goal for X-ICT is to learn cross-lingual retrieval in which the model learns to identify the passage corresponding to a given translated title. On the X-Fact dataset, our approach achieves 2.23% absolute F1 improvement in the zero-shot cross-lingual setup over prior systems. The source code and data are publicly available at https://github.com/khuangaf/CONCRETE.- Anthology ID:
- 2022.coling-1.86
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1024–1035
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.86
- DOI:
- Cite (ACL):
- Kung-Hsiang Huang, ChengXiang Zhai, and Heng Ji. 2022. CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1024–1035, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval (Huang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.coling-1.86.pdf
- Code
- khuangaf/concrete
- Data
- X-Fact