Abstract
We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent and compare documents: (1) multilingual topic model; (2) cross-lingual document embeddings; and (3) Wasserstein distance.We test the performance of these methods in retrieving news articles in Swedish that are known to be related to a given Finnish article.The results show that ensembles of the methods outperform the stand-alone methods, suggesting that they capture complementary characteristics of the documents- Anthology ID:
- 2020.clssts-1.6
- Volume:
- Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Venue:
- CLSSTS
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 32–37
- Language:
- English
- URL:
- https://aclanthology.org/2020.clssts-1.6
- DOI:
- Cite (ACL):
- Elaine Zosa, Mark Granroth-Wilding, and Lidia Pivovarova. 2020. A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval. In Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020), pages 32–37, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval (Zosa et al., CLSSTS 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.clssts-1.6.pdf