# Putting Natural in Natural Language Processing

arXiv: https://arxiv.org/abs/2305.04572 · HTML: https://mlsanity.com/paper/2305.04572/

## Key facts

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

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://mlsanity.com/paper/2005.00955/) (2020)
- [Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision](https://mlsanity.com/paper/2010.06775/) (2020)
- [The Zero Resource Speech Challenge 2021: Spoken language modelling](https://mlsanity.com/paper/2104.14700/) (2021)
- [Word Discovery in Visually Grounded, Self-Supervised Speech Models](https://mlsanity.com/paper/2203.15081/) (2022)
- [WER we are and WER we think we are](https://mlsanity.com/paper/2010.03432/) (2020)
- [Learning English with Peppa Pig](https://mlsanity.com/paper/2202.12917/) (2022)
- [Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives](https://mlsanity.com/paper/2301.10761/) (2023)

## Built on by (most cited first, 17 of 17)

- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- [Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers](https://mlsanity.com/paper/2310.09925/) (2023)
- [Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language](https://mlsanity.com/paper/2404.04809/) (2024)
- [ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation](https://mlsanity.com/paper/2310.11282/) (2023)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects](https://mlsanity.com/paper/2510.07890/) (2025)
- [Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech](https://mlsanity.com/paper/2406.12621/) (2024)
- [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025)
- [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025)
- [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025)
- [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025)
- [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026)
- [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026)
- [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026)
- [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026)

## 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.