Papers by Jonathan Rawski

2 papers
Benchmarking Compositionality with Formal Languages (2022.coling-1)

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Challenge: Compositionality is a hallmark of human language, but it is not yet fully understood . recombining known primitive concepts into larger novel combinations is elusive .
Approach: They use finite-state transducers to make a dataset with controllable compositionality . they find that the models either learn the relations completely or not at all .
Outcome: The proposed model learns the relation completely or not at all on large datasets.
Transformers as Transducers (2025.tacl-1)

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Challenge: Using finite transducers, we find that transformers can express large classes of (total functional) transductions.
Approach: They extend existing RASP programming language to sequence-to-sequence transductions and introduce two new extensions.
Outcome: The proposed model can express surprisingly large classes of (total functional) transductions.

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