# Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars

arXiv: https://arxiv.org/abs/2109.04939 · HTML: https://mlsanity.com/paper/2109.04939/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Lower Perplexity is Not Always Human-Like](https://mlsanity.com/paper/2106.01229/) (2021)
- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Finding Syntax in Human Encephalography with Beam Search](https://mlsanity.com/paper/1806.04127/) (2018)
- [Effective Inference for Generative Neural Parsing](https://mlsanity.com/paper/1707.08976/) (2017)
- [Structural Supervision Improves Learning of Non-Local Grammatical Dependencies](https://mlsanity.com/paper/1903.00943/) (2019)
- [A Neural Model of Adaptation in Reading](https://mlsanity.com/paper/1808.09930/) (2018)
- [What Do Recurrent Neural Network Grammars Learn About Syntax?](https://mlsanity.com/paper/1611.05774/) (2016)
- [Effective Batching for Recurrent Neural Network Grammars](https://mlsanity.com/paper/2105.14822/) (2021)

## Built on by (most cited first, 5 of 5)

- [Context Limitations Make Neural Language Models More Human-Like](https://mlsanity.com/paper/2205.11463/) (2022)
- [Large Language Models Are Human-Like Internally](https://mlsanity.com/paper/2502.01615/) (2025)
- [Emergent Word Order Universals from Cognitively-Motivated Language Models](https://mlsanity.com/paper/2402.12363/) (2024)
- [Psychometric Predictive Power of Large Language Models](https://mlsanity.com/paper/2311.07484/) (2023)
- [Composition, Attention, or Both?](https://mlsanity.com/paper/2210.12958/) (2022)

## Abstract (arXiv)

In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.