Cross-Lingual Learning-to-Rank with Shared Representations
Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, Kentaro Inui
Abstract
Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user’s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.- Anthology ID:
- N18-2073
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 458–463
- Language:
- URL:
- https://aclanthology.org/N18-2073
- DOI:
- 10.18653/v1/N18-2073
- Cite (ACL):
- Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, and Kentaro Inui. 2018. Cross-Lingual Learning-to-Rank with Shared Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 458–463, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Learning-to-Rank with Shared Representations (Sasaki et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/N18-2073.pdf
- Data
- Large-Scale CLIR Dataset