UNIOR NLP at MWSA Task - GlobaLex 2020: Siamese LSTM with Attention for Word Sense Alignment
Raffaele Manna, Giulia Speranza, Maria Pia di Buono, Johanna Monti · The Oriental (University of Naples - L'Orientale) · 2020
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 pre-trained 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 subset 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 with attention and PoS tagging (LSTM-A) performed better than the other two systems, achieving a 5-Class Accuracy score of 0.844 in the Overall Results, ranking the first position among five teams.