Papers by Eleni Metheniti
Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)
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| Challenge: | Discourse analysis is a hard task, but data is limited for other languages. |
| Approach: | They propose to use zero-shot learning to combine discourse relation data . they compare two versions of the same text with different labels . |
| Outcome: | The proposed method can be applied to languages, frameworks, or similarity measures. |
How Relevant Are Selectional Preferences for Transformer-based Language Models? (2020.coling-main)
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| Challenge: | Selectional preference is defined as the tendency of a predicate to favor particular arguments within a certain linguistic context and reject others that result in conflicting or implausible meanings. |
| Approach: | They examine the probability that Bert assigns a dependent word given the presence of a head word in a sentence to determine whether selectional preference exists. |
| Outcome: | The proposed model is based on the SP-10K corpus of selectional preference and the ukWaC corpus. |
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus (2020.lrec-1)
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| Challenge: | Inflectional corpora with annotated morpheme boundaries are scarce in the NLP community . a generated, multilingual inflectional lexicon with morphological features is not as good as UniMorph's . |
| Approach: | They evaluate a multilingual inflectional corpus with morpheme boundaries from the English Wiktionary and the UniMorph project's inflection corpus. |
| Outcome: | The generated Wikinflection corpus is not as good as UniMorph's, but extracts significant amount of words from the intersection of the two corpora. |
“Chère maison” or “maison chère”? Transformer-based prediction of adjective placement in French (2023.findings-eacl)
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| Challenge: | Prior work has shown that transformer-based language models are insensitive to permutated word order, but this is not the case with pretraining. |
| Approach: | They conduct experiments to assess whether transformer-based language models are able to learn the adjective position in noun phrases in French. |
| Outcome: | The proposed model is weaker with complex structures and fixed expressions, but favors context and global syntactic roles. |