Summary of Modeling Bilingual Sentence Processing: Evaluating Rnn and Transformer Architectures For Cross-language Structural Priming, by Demi Zhang et al.
Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Priming
by Demi Zhang, Bushi Xiao, Chao Gao, Sangpil Youm, Bonnie J Dorr
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recurrent Neural Network (RNN) and Transformer models are evaluated in replicating cross-language structural priming, a key indicator of abstract grammatical representations in human language processing. The study focuses on Chinese-English priming, which involves two typologically distinct languages, to 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. The findings indicate that transformers outperform RNNs in generating primed sentence structures, with accuracy rates exceeding 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how artificial intelligence models can understand and mimic the way humans process language. It tests two types of AI models, called RNNs and Transformers, to see which one does better at something called “structural priming.” This is when you’re shown a sentence structure, like “He ran quickly,” and then asked to come up with another sentence using a similar structure. The study finds that the Transformer model does a lot better than the RNN model at this task. This is important because it helps us understand how AI models can be used to study human language processing. |
Keywords
» Artificial intelligence » Likelihood » Neural network » Rnn » Transformer
