Leonardo Ranaldi


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Lacking the Embedding of a Word? Look it up into a Traditional Dictionary
Elena Sofia Ruzzetti | Leonardo Ranaldi | Michele Mastromattei | Francesca Fallucchi | Noemi Scarpato | Fabio Massimo Zanzotto
Findings of the Association for Computational Linguistics: ACL 2022

Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.


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KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations
Fabio Massimo Zanzotto | Andrea Santilli | Leonardo Ranaldi | Dario Onorati | Pierfrancesco Tommasino | Francesca Fallucchi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Syntactic parsers have dominated natural language understanding for decades. Yet, their syntactic interpretations are losing centrality in downstream tasks due to the success of large-scale textual representation learners. In this paper, we propose KERMIT (Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees) to embed symbolic syntactic parse trees into artificial neural networks and to visualize how syntax is used in inference. We experimented with KERMIT paired with two state-of-the-art transformer-based universal sentence encoders (BERT and XLNet) and we showed that KERMIT can indeed boost their performance by effectively embedding human-coded universal syntactic representations in neural networks