--- title: "Steven Y. Feng · Stanford University — author profile and papers" description: "Steven Y. Feng, Stanford University: 2 papers on Elamur Research (2021–2024), h-index 6 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/steven-y-feng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/steven-y-feng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Steven Y. Feng # Steven Y. Feng Steven Y. Feng works in natural language processing, with a focus on the training data that language models learn from: how that data can be expanded or varied, and whether its character matters as much as its sheer volume. Their papers here, from 2021 to 2024, fall under [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [Psycholinguistics & language acquisition](https://md.research.elamur.ai/free/en/topics/psycholinguistics/). They combine broad reviews of existing techniques with controlled experiments in which models are trained from scratch. The most cited of these is [A Survey of Data Augmentation Approaches for NLP](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/) (2021). It asks why augmentation is routine in computer vision yet less developed for text, where inputs are discrete and a transformation can easily change an example's label. The survey weighs how simple a method is to apply against how much it improves results, and it points to the limited theory behind why augmentation works. It sorts techniques into three groups: rule-based edits, MixUp-style blending of examples, and generation by models, for instance through backtranslation. The more recent work turns toward language acquisition. [Is Child-Directed Speech Effective Training Data for Language Models?](https://md.research.elamur.ai/free/en/papers/feng-2024-is-child-directed-speech-effective/) (2024) asks whether the speech addressed to children explains why they learn language from far less input than models need. GPT-2 and RoBERTa are trained on English CHILDES, and the results are compared with a synthetic dialogue corpus written by GPT-4 and with subtitle, Wikipedia and BabyLM text. Syntactic knowledge is tested with the Zorro benchmark and meaning with word-similarity tasks. The study also looks separately at the effect of presenting conversations in order of the child's age. Feng is based at [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) and has earlier papers from [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) and the [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/). Co-authors include [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/), Varun Gangal, Teruko Mitamura, Soroush Vosoughi and Jason Wei, as well as [Michael C. Frank](https://md.research.elamur.ai/free/en/authors/michael-c-frank/) and Noah D. Goodman. Steven Y. Feng has 2 papers in our library, in 2021–2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Psycholinguistics & language acquisition](https://md.research.elamur.ai/free/en/topics/psycholinguistics/); often with [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/), [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/), [Michael C. Frank](https://md.research.elamur.ai/free/en/authors/michael-c-frank/). 2 papers in our library · 2021–2024 [OpenAlex](https://openalex.org/A5039277252) · [**h-index 6**](https://openalex.org/authors/A5039277252) · [15 works](https://openalex.org/works?filter=author.id:A5039277252) · [796 citations](https://openalex.org/works?filter=author.id:A5039277252&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/steven-y-feng/#papers) · [🔗 Cited 295 times in our library](https://md.research.elamur.ai/free/en/authors/steven-y-feng/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #2281](https://md.research.elamur.ai/free/en/paper-stars/page/23/#star-2281) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Steven+Y.+Feng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Steven+Y.+Feng) · [dblp](https://dblp.org/search?q=Steven+Y.+Feng) · [ACL Anthology](https://aclanthology.org/search/?q=Steven+Y.+Feng) ## Affiliations [🎓**Stanford University** Stanford · United States 2021–2026 · 20 papers*current*](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [🎓**Carnegie Mellon University** Pittsburgh · United States 2020–2022 · 7 papers](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [🎓**University of Waterloo** Waterloo · Canada 2019–2020 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 4 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Artificial Intelligence · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/) 🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Psycholinguistics & language acquisition · 1 work](https://md.research.elamur.ai/free/en/topics/psycholinguistics/) 💡 Concepts 1 Key ideas and methods explained on our concept pages.[Child-directed speech · 1 work](https://md.research.elamur.ai/free/en/concepts/child-directed-speech/) ## Works by year ### 2020s [**2024** 1 paper Steven Y. Feng's papers of 2024](https://md.research.elamur.ai/free/en/papers/feng-2024-is-child-directed-speech-effective/) · [**2021** 1 paper Steven Y. Feng's papers of 2021](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/) ## Papers The most cited first (OpenAlex and our library). 1. **[A Survey of Data Augmentation Approaches for NLP](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)** *(2021 · 937 citations)* This survey maps the field of data augmentation (DA) for NLP. DA means enlarging or diversifying training material without gathering new labelled data. The authors argue that text is harder to augment than images because it is discrete: you cannot add continuous noise, and small edits can easily change meaning or… 2. **[Is Child-Directed Speech Effective Training Data for Language Models?](https://md.research.elamur.ai/free/en/papers/feng-2024-is-child-directed-speech-effective/)** *(2024 · 18 citations)* Children acquire language after hearing thousands of times fewer words than modern language models require. ## All works 3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them. 1. [**A Survey of Data Augmentation Approaches for NLP arXiv:2105.03075**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 937 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/) 2. **[Is Child-Directed Speech Effective Training Data for Language Models?](https://md.research.elamur.ai/free/en/papers/feng-2024-is-child-directed-speech-effective/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Steven Y. Feng, Noah D. Goodman, Michael C. Frank*2024 · 18 citations* 3. **[Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining ↗](https://arxiv.org/abs/2412.15285) arXiv:2412.15285 · no summary yet**Steven Feng et al.*2024 · 12 citations* ## Co-authors here 7 co-authors with a page, the most papers together first. - [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/) - [Jason Wei](https://md.research.elamur.ai/free/en/authors/jason-wei/) - [Michael C. Frank](https://md.research.elamur.ai/free/en/authors/michael-c-frank/) - [Noah D. Goodman](https://md.research.elamur.ai/free/en/authors/noah-d-goodman/) - [Soroush Vosoughi](https://md.research.elamur.ai/free/en/authors/soroush-vosoughi/) - [Teruko Mitamura](https://md.research.elamur.ai/free/en/authors/teruko-mitamura/) - [Varun Gangal](https://md.research.elamur.ai/free/en/authors/varun-gangal/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/) ## Cited by Papers of our library cite their works 295 times (of the 141,840 papers whose bibliographies we read); [17 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/steven-y-feng/cited-by/). Updated 24 October 2026