Papers by Roberto Zamparelli
The Functional Relevance of Probed Information: A Case Study (2023.eacl-main)
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| Challenge: | Recent studies have shown that transformer models like BERT rely on number information encoded in their representations of sentences’ subjects and head verbs when performing subject-verb agreement. |
| Approach: | They propose to use probing to find out which words contain functionally relevant information encoded in the representations of subject plurality and words that agree with it in number in BERT. |
| Outcome: | The proposed model only uses the subject plurality information encoded in its representations of the subject and words that agree with it in number. |
RNN Simulations of Grammaticality Judgments on Long-distance Dependencies (C18-1)
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| Challenge: | LSTM networks can detect linguistic structures which are ungrammatical due to extraction violations, but are sensitive to linguistic processing factors. |
| Approach: | They propose to use LSTM networks to detect ungrammatical sentences by detecting extra arguments and subject-relative clause island violations. |
| Outcome: | The proposed model can correctly classify (un)grammatical sentences, in certain conditions, but is sensitive to linguistic processing factors and unable to induce a more abstract notion of grammaticality. |