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PAR-MEX Shared Task Submission Description: Identifying Spanish Paraphrases Using Pretrained Models and Translations

This paper describes our participation in the Paraphrase Identification in Mexican Spanish (PAR-MEX) shared task [1]. We show that publicly available, pretrained models already achieve very strong performance on this task (ranking 2nd among all submissions and achieving 0.9373 f1 score on test data) and can be used in a data efficient way. The performance is good despite domain specificity of the data and linguistic differences, e.g. Mexican Spanish vs. standard Spanish. Furthermore, we show that translation to English allows for using further pretrained models with stronger performance. We conclude that combining different pretrained and multilingual models is a currently strong and efficient approach to paraphrase identification, and paraphrase identification will likely benefit more from stronger pretrained models than from specialised methods.