mlsanityData to 7 Oct 2026

Putting Natural in Natural Language Processing

arXiv 2305.04572 ↗First posted 8 May 2023cs.CL cs.AI eess.AScited by 17

Not on the map. Why?

Abstract (arXiv)

Human language is firstly spoken and only secondarily written. Text, however, is a very convenient and efficient representation of language, and modern civilization has made it ubiquitous. Thus the field of NLP has overwhelmingly focused on processing written rather than spoken language. Work on spoken language, on the other hand, has been siloed off within the largely separate speech processing community which has been inordinately preoccupied with transcribing speech into text. Recent advances in deep learning have led to a fortuitous convergence in methods between speech processing and mainstream NLP. Arguably, the time is ripe for a unification of these two fields, and for starting to take spoken language seriously as the primary mode of human communication. Truly natural language processing could lead to better integration with the rest of language science and could lead to systems which are more data-efficient and more human-like, and which can communicate beyond the textual modality.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

14 of 34 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2022Emergent Abilities of Large Language Modelscited by 2,483
  6. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  7. 2021Generative Spoken Language Modeling from Raw Audiocited by 336
  8. 2020How Can We Accelerate Progress Towards Human-like Linguistic Generalization?cited by 114
  9. 2020Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervisioncited by 93
  10. 2021The Zero Resource Speech Challenge 2021: Spoken language modellingcited by 47
  11. 2022Word Discovery in Visually Grounded, Self-Supervised Speech Modelscited by 37
  12. 2020WER we are and WER we think we arecited by 19
2 more
  1. 2022Learning English with Peppa Pigcited by 8
  2. 2023Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectivescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 7; 2026: 4.

17 papers.

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  1. 2025How Linguistics Learned to Stop Worrying and Love the Language Modelscited by 50
  2. 2023HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Modelscited by 41
  3. 2024What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialectscited by 20
  4. 2023Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformerscited by 13
  5. 2024Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Languagecited by 12
  6. 2023ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formationcited by 3
  7. 2025Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participationcited by 3
  8. 2025Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialectscited by 2
  9. 2024Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speechcited by 0
  10. 2025Language models as tools for investigating the distinction between possible and impossible natural languagescited by 0
  11. 2025Large language models have learned to use languagecited by 0
  12. 2025Large language models are not about natural languagecited by 0
5 more
  1. 2025Linguists should learn to love speech-based deep learning modelscited by 0
  2. 2026Are Language Models Models?cited by 0
  3. 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilitiescited by 0
  4. 2026You Can't Fight in Here! This is BBS!cited by 0
  5. 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appearcited by 0