Papers by Michele Mastromattei
Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages (2023.findings-emnlp)
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Elena Ruzzetti, Federico Ranaldi, Felicia Logozzo, Michele Mastromattei, Leonardo Ranaldi, Fabio Zanzotto
| Challenge: | a recent study examined how models for typologically similar languages encode structural information. |
| Approach: | They propose to layer-wise compare transformers for typologically similar languages to observe similarities . they use a domain adaptation on semantically equivalent texts to measure similarity . |
| Outcome: | The proposed model outperforms all other models on unseen sentences . the proposed model is based on a typologically similar language . |
Every time I fire a conversational designer, the performance of the dialogue system goes down (2022.lrec-1)
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Giancarlo Xompero, Michele Mastromattei, Samir Salman, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. |
| Approach: | They propose a system where domain scripts are coded in semi-logical rules and evaluated semi-logic rules produced by differently-skilled conversational designers. |
| Outcome: | The proposed system outperforms state-of-the-art systems when trained with smaller sets of annotated dialogues. |
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models (2024.findings-acl)
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| Challenge: | Existing pruning algorithms suffer from limitations such as architecture specificity and reliance on demanding calculations. |
| Approach: | They propose a pruning algorithm based on Kernel Density Estimation . it preserves most significant parameters while restoring others to their pre-training state . |
| Outcome: | The proposed pruning algorithm achieves better performance than the original unpruned version. |
Lacking the Embedding of a Word? Look it up into a Traditional Dictionary (2022.findings-acl)
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Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei, Francesca Fallucchi, Noemi Scarpato, Fabio Massimo Zanzotto
| Challenge: | Word embeddings are powerful dictionaries, but they fail to give sense to rare words . a large body of research is devoted to devising ways to capture word meaning . |
| Approach: | They propose to use definitions retrieved from traditional dictionaries to build word embeddings for rare words. |
| Outcome: | The proposed methods outperform state-of-the-art methods for embeddings of unknown words . the proposed methods significantly outperformed the BERT method for OOV words compared to the proposed method . |