# What can linguistics and deep learning contribute to each other?

arXiv: https://arxiv.org/abs/1809.04179 · HTML: https://mlsanity.com/paper/1809.04179/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [Dissociating language and thought in large language models](https://mlsanity.com/paper/2301.06627/) (2023)
- [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://mlsanity.com/paper/2109.12053/) (2021)
- [What Can String Probability Tell Us About Grammaticality?](https://mlsanity.com/paper/2510.16227/) (2025)
- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [Linguistically inspired roadmap for building biologically reliable protein language models](https://mlsanity.com/paper/2207.00982/) (2022)
- [Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models](https://mlsanity.com/paper/2505.05970/) (2025)
- [Quantum Transfer Learning for Acceptability Judgements](https://mlsanity.com/paper/2401.07777/) (2024)
- [Predictability and Causality in Spanish and English Natural Language Generation](https://mlsanity.com/paper/2408.14283/) (2024)

## Abstract (arXiv)

Joe Pater's target article calls for greater interaction between neural network research and linguistics. I expand on this call and show how such interaction can benefit both fields. Linguists can contribute to research on neural networks for language technologies by clearly delineating the linguistic capabilities that can be expected of such systems, and by constructing controlled experimental paradigms that can determine whether those desiderata have been met. In the other direction, neural networks can benefit the scientific study of language by providing infrastructure for modeling human sentence processing and for evaluating the necessity of particular innate constraints on language acquisition.