Raffaele Manna author Giulia Speranza author Maria Pia di Buono author Johanna Monti author 2020-may text English eng European Language Resources Association Marseille, France conference publication 979-10-95546-46-7 In this paper we describe the system submitted to the ELEXIS Monolingual Word Sense Alignment Task. We test different systems,which are two types of LSTMs and a system based on a pretrained Bidirectional Encoder Representations from Transformers (BERT)model, to solve the task. LSTM models use fastText pre-trained word vectors features with different settings. For training the models,we did not combine external data with the dataset provided for the task. We select a sub-set of languages among the proposed ones,namely a set of Romance languages, i.e., Italian, Spanish, Portuguese, together with English and Dutch. The Siamese LSTM withattention and PoS tagging (LSTM-A) performed better than the other two systems, achieving a 5-Class Accuracy score of 0.844 in theOverall Results, ranking the first position among five teams. manna-etal-2020-unior https://www.aclweb.org/anthology/2020.globalex-1.13 2020-may 76 83