Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Priming

TL;DR

This paper compares RNN and Transformer models in simulating cross-language structural priming between Chinese and English, revealing Transformers' superior performance and implications for understanding human language processing.

Contribution

It is the first to evaluate RNN and Transformer models specifically on cross-language structural priming involving typologically distinct languages.

Findings

Transformers outperform RNNs in priming accuracy.

Priming accuracy exceeds 25.84% to 33.33% with Transformers.

Results suggest cue-based retrieval mechanisms in language processing.

Abstract

This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer models in replicating cross-language structural priming, a key indicator of abstract grammatical representations in human language processing. Focusing on Chinese-English priming, which involves two typologically distinct languages, we examine how these models handle the robust phenomenon of structural priming, where exposure to a particular sentence structure increases the likelihood of selecting a similar structure subsequently. Our findings indicate that transformers outperform RNNs in generating primed sentence structures, with accuracy rates that exceed 25.84\% to 33. 33\%. This challenges the conventional belief that human sentence processing primarily involves recurrent and immediate processing and suggests a role for cue-based retrieval mechanisms. This work contributes to our understanding of how computational models may reflect human cognitive processes across diverse language families.