Papers by Mycal Tucker

2 papers
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes (2022.naacl-main)

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Challenge: Recent studies show that models encode syntactic information redundantly . this allows researchers to boost models' performance by injecting syntaktic information into embeddings .
Approach: They propose a new probe design that guides probes to consider all syntactic information present in embeddings.
Outcome: The proposed model improves performance by injecting syntactic information into models.
What if This Modified That? Syntactic Interventions with Counterfactual Embeddings (2021.findings-acl)

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Challenge: Prior art aims to uncover meaningful properties within model representations, but it is unclear how faithfully such probes portray information that the models actually use.
Approach: They propose a technique for generating counterfactual embeddings within models . they produce evidence that some models use a tree-distancelike representation of syntax .
Outcome: The proposed technique produces evidence that some models use tree-distancelike representations of syntax in downstream prediction tasks.

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