The task consists in rating the degree of semantic equivalence between two text snippets.
Publication
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015. SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 252–263, Denver, Colorado. Association for Computational Linguistics.
Competition
Language
Spanish
English
NLP topic
Abstract task
Dataset
Year
2015
Publication link
Task results
| System | Precision | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation Sort ascending | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ExBThemis-trainEs | 0.6900 | ||||||||||||||||||||||||||||||||||||
| ExBThemis-trainMini | 0.6890 | ||||||||||||||||||||||||||||||||||||
| ExBThemis-trainEn | 0.6720 | ||||||||||||||||||||||||||||||||||||
| UMDuluth-BlueTeam-run1 | 0.6340 | ||||||||||||||||||||||||||||||||||||
| SopaLipnIimas-RF | 0.5650 |
