Abstract
We investigate the capabilities of pre-trained models, without any fine-tuning, for a document-level multilingual news similarity task of SemEval-2022. We utilize title and news content with appropriate pre-processing techniques. Our system derives 14 different similarity features using a combination of state-of-the-art methods (MPNet) with well-known statistical methods (i.e. TF-IDF, Word Mover’s distance). We formulate multilingual news similarity task as a regression task and approximate the overall similarity between two news articles using these features. Our best-performing system achieved a correlation score of 70.1% and was ranked 20th among the 34 participating teams. In this paper, in addition to a system description, we also provide further analysis of our results and an ablation study highlighting the strengths and limitations of our features. We make our code publicly available at https://github.com/cicl-iscl/multinewssimilarity- Anthology ID:
- 2022.semeval-1.165
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1171–1177
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.165
- DOI:
- 10.18653/v1/2022.semeval-1.165
- Cite (ACL):
- Mayank Jobanputra and Lorena Martín Rodríguez. 2022. OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1171–1177, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches (Jobanputra & Martín Rodríguez, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.semeval-1.165.pdf
- Code
- cicl-iscl/multinewssimilarity