Formal Language Theory Meets Modern NLP
Not on the map. Why?
Abstract (arXiv)
NLP is deeply intertwined with the formal study of language, both conceptually and historically. Arguably, this connection goes all the way back to Chomsky's Syntactic Structures in 1957. It also still holds true today, with a strand of recent works building formal analysis of modern neural networks methods in terms of formal languages. In this document, I aim to explain background about formal languages as they relate to this recent work. I will by necessity ignore large parts of the rich history of this field, instead focusing on concepts connecting to modern deep learning-based NLP.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 26 of its 69 references that are in the map. First posted on arXiv 19 Feb 2021.
Builds on
26 of 69 references.
References · 26
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14 more
- 2019LSTM Networks Can Perform Dynamic Countingcited by 45
- 2019Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languagescited by 31
- 2018Rational Recurrencescited by 18
- 2020A Survey of Neural Networks and Formal Languagescited by 17
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- 2018Context-Free Transductions with Neural Stackscited by 14
- 2020On the Ability and Limitations of Transformers to Recognize Formal Languagescited by 12
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Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 2.
9 papers.
- 2021Saturated Transformers are Constant-Depth Threshold Circuitscited by 137
- 2023What Formal Languages Can Transformers Express? A Surveycited by 120
- 2022What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environmentcited by 9
- 2023Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languagescited by 2
- 2024Extracting Moore Machines from Transformers using Queries and Counterexamplescited by 2
- 2022Implementation of general formal translatorscited by 1
- 2021Learning and Generalization in RNNscited by 0
- 2023Nondeterministic Stacks in Neural Networkscited by 0
- 2024Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networkscited by 0
- 2024Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks20 Dec 2024
- 2024Extracting Moore Machines from Transformers using Queries and Counterexamples8 Oct 2024
- 2023What Formal Languages Can Transformers Express? A Survey1 Nov 2023
- 2023Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages2 Sep 2023
- 2023Nondeterministic Stacks in Neural Networks25 Apr 2023
- 2022Implementation of general formal translators16 Dec 2022
- 2022What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment19 Apr 2022
- 2021Saturated Transformers are Constant-Depth Threshold Circuits30 Jun 2021
- 2021Learning and Generalization in RNNs31 May 2021