What can linguistics and deep learning contribute to each other?
Not on the map. Why?
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 3.
10 papers.
- 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 97
- 2025Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 86
- 2023Dissociating language and thought in large language modelscited by 27
- 2021Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpuscited by 18
- 2025What Can String Probability Tell Us About Grammaticality?cited by 16
- 2023Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanitiescited by 9
- 2022Linguistically inspired roadmap for building biologically reliable protein language modelscited by 7
- 2025Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Modelscited by 4
- 2024Quantum Transfer Learning for Acceptability Judgementscited by 2
- 2024Predictability and Causality in Spanish and English Natural Language Generationcited by 0
- 2025What Can String Probability Tell Us About Grammaticality?17 Oct 2025
- 2025Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models9 May 2025
- 2025Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora10 Apr 2025
- 2024Predictability and Causality in Spanish and English Natural Language Generation26 Aug 2024
- 2024Quantum Transfer Learning for Acceptability Judgements15 Jan 2024
- 2023Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities30 Oct 2023
- 2023Dissociating language and thought in large language models16 Jan 2023
- 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisition17 Aug 2022
- 2022Linguistically inspired roadmap for building biologically reliable protein language models3 Jul 2022
- 2021Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus24 Sep 2021