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.

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
2020

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11574/192549
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