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    <abstract>Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser’s prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.</abstract>
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%0 Conference Proceedings
%T SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
%A Prasad, Grusha
%A Linzen, Tal
%Y Barak, Libby
%Y Alikhani, Malihe
%S Proceedings of the 28th Conference on Computational Natural Language Learning
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F prasad-linzen-2024-spawning
%X Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser’s prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.
%U https://aclanthology.org/2024.conll-1.14
%P 178-197

Markdown (Informal)

[SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser](https://aclanthology.org/2024.conll-1.14) (Prasad & Linzen, CoNLL 2024)

• SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser (Prasad & Linzen, CoNLL 2024)

ACL

• Grusha Prasad and Tal Linzen. 2024. SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser. In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 178–197, Miami, FL, USA. Association for Computational Linguistics.