| Resumen |
In this paper, we work on Paraphrase Identification in Mexican Spanish (PAR-MEX) at the sentence level. We introduced two lightweight methods, linear regression and multilayer perceptron for training data on features, extracted from pre-trained models. A rule of thumb, pair similarity is used to filter noises in the positive examples. We obtained the best F1 of 88.67%, which points out the effectiveness of traditional methods with the support of pre-trained models. In the challenge, our result ranked fourth in the organizers' result table. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
