Papers by Tim Van de Cruys

5 papers
DiscSense: Automated Semantic Analysis of Discourse Markers (2020.lrec-1)

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Challenge: Existing models for predicting discourse markers have been used to study link between markers and semantic relations .
Approach: They use a model trained to predict discourse markers between sentence pairs to predict plausible markers between sentences with a known semantic relation.
Outcome: The proposed method predicts markers between sentence pairs with a known semantic relation . the resulting dataset, named DiscSense, is publicly available .
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.
“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.
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding (2022.lrec-1)

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Challenge: a number of studies have suggested that models induce universal text representations . current benchmarks focus on semantic phenomena, so pragmatics needs to be the focus .
Approach: They propose a benchmark that unites 11 pragmatics-focused evaluation datasets for English.
Outcome: The proposed benchmark shows that natural language inference does not result in genuinely universal representations.
Automatic Poetry Generation from Prosaic Text (2020.acl-main)

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Challenge: In recent years, successful approaches have emerged to accurately model various aspects of natural language.
Approach: They propose to combine neural networks with a poetry generation system that only uses standard text as input . they use standard text to model syntactic well-formedness and topical coherence .
Outcome: The proposed framework is applied to the generation of poems in English and French . it uses standard, non-poetic text and its output is constrained to confer a poetic character .

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