Papers by Lorenzo Bertolini
Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
| Approach: | They propose two techniques which generate new training examples from existing ones . they combine linguistic principles of hypernym transitivity and intersective modifier-noun composition . |
| Outcome: | The proposed techniques generate new training examples from existing datasets. |
Representing Syntax and Composition with Geometric Transformations (2021.findings-acl)
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| Challenge: | Existing models of word meaning are based on syntactic rather than proximal co-occurrences, but they are not suitable for syntax sensitive composition. |
| Approach: | They propose to encode syntactic structure by extending the Skip-Gram with Negative sampling architecture from word2vec. |
| Outcome: | The proposed models perform favourably on benchmark word similarity tasks on similarity tests on similar words compared to models based on proximal co-occurrence . however, the real promise of distributional models is the potential for syntax-sensitive composition. |
Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment (2022.coling-1)
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| Challenge: | Existing studies have shown that pre-trained large language models acquire knowledge during pre-training which enables reasoning over relationships between words and more complex inferences over larger units of meaning. |
| Approach: | They propose a benchmark to test compositional entailment models using adjective-noun phrases. |
| Outcome: | The proposed model can generalise well to out–of–distribution sets, since the required knowledge can be stored in the representations of subwords (SW) tokens. |