OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches

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

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