--- title: "Chan Young Park — author profile and papers" description: "Chan Young Park, Microsoft (United States): 2 papers on Elamur Research (2021–2023), h-index 9 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/chan-young-park/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/chan-young-park/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Chan Young Park # Chan Young Park 2 papers on this site · 2021–2023 current affiliation [Microsoft (United States)](https://ror.org/00d0nc645), Redmond, United States; [Microsoft Research (United Kingdom)](https://ror.org/05k87vq12), Cambridge, United Kingdom; [Texas A&M University](https://ror.org/01f5ytq51), College Station, United States Chan Young Park has 2 papers on this site, in 2021–2023, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/), [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/); often with [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/), [David R. Mortensen](https://md.research.elamur.ai/free/en/authors/david-r-mortensen/). [OpenAlex](https://openalex.org/A5100608515) · [ORCID](https://orcid.org/0000-0002-2720-0112) · [**h-index 9**](https://openalex.org/authors/A5100608515) · [41 works](https://openalex.org/works?filter=author.id:A5100608515) · [331 citations](https://openalex.org/works?filter=author.id:A5100608515&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works on this site](https://md.research.elamur.ai/free/en/authors/chan-young-park/#papers) · [256 citations in our library](https://md.research.elamur.ai/free/en/authors/chan-young-park/cited-by/) · 3 more works in our library 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=Chan+Young+Park) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Chan+Young+Park) · [dblp](https://dblp.org/search?q=Chan+Young+Park) · [ACL Anthology](https://aclanthology.org/search/?q=Chan+Young+Park) ## Affiliations - [Microsoft (United States)](https://ror.org/00d0nc645) · Redmond, United States —current · 2026 on 2 papers - [Microsoft Research (United Kingdom)](https://ror.org/05k87vq12) · Cambridge, United Kingdom —current · 2026 on 1 paper - [Texas A&M University](https://ror.org/01f5ytq51) · College Station, United States —current · 2026 on 1 paper - [University of Washington](https://ror.org/00cvxb145) · Seattle, United States —2024–2025 on 5 papers - [The Ohio State University](https://ror.org/00rs6vg23) · Columbus, United States —2024–2025 on 2 papers - [Carnegie Mellon University](https://ror.org/05x2bcf33) · Pittsburgh, United States —2020–2024 on 12 papers - [University of Michigan](https://ror.org/00jmfr291) · Ann Arbor, United States —2021 on 2 papers From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Topics [Language models & pretraining 1](https://md.research.elamur.ai/free/en/topics/language-models/) · [Sentiment & emotion 1](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Multilingual & cross-lingual NLP 1](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Bias, fairness & safety 1](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Idioms & multiword expressions 1](https://md.research.elamur.ai/free/en/topics/multiword-expressions/) ## Papers The most cited first (OpenAlex and our library). 1. **[From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)** *(2023 · 305 citations)* Large language models absorb text from news, forums, books and encyclopedias, and much of that text carries political opinions. 2. **[Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks](https://md.research.elamur.ai/free/en/papers/sun-2021-cross-cultural-similarity-features-cross/)** *(2021 · 28 citations)* Choosing a source language for cross-lingual transfer is usually guided by typological or genealogical closeness. ## Also in our library 3 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 2 co-authors with a page, the most papers together first. - [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/) ×2 - [David R. Mortensen](https://md.research.elamur.ai/free/en/authors/david-r-mortensen/) ## Cited by Papers of our library cite their works 256 times (of the 140 884 papers whose bibliographies we read); [13 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/chan-young-park/cited-by/).