---
title: "Yejin Choi — papers and cards"
description: "Yejin Choi: 5 paper cards in the Research library (2017–2024), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/yejin-choi/"
---
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# Yejin Choi

5 paper cards in the Research library · 2017–2024

Yejin Choi wrote 5 of the papers Mova reads, in 2017–2024, most of them on [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/); often with [Chandra Bhagavatula](https://md.research.elamur.ai/free/en/authors/chandra-bhagavatula/), [Ronan Le Bras](https://md.research.elamur.ai/free/en/authors/ronan-le-bras/), [Keisuke Sakaguchi](https://md.research.elamur.ai/free/en/authors/keisuke-sakaguchi/).

[OpenAlex](https://openalex.org/A5102992157) · [ORCID](https://orcid.org/0000-0003-3032-5378) · [**h-index 84**](https://openalex.org/authors/A5102992157) · [33876 citations](https://openalex.org/authors/A5102992157) · [437 works](https://openalex.org/works?filter=author.id:A5102992157) — openalex-snapshot, 2026-10-14

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=Yejin+Choi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yejin+Choi) · [dblp](https://dblp.org/search?q=Yejin+Choi) · [ACL Anthology](https://aclanthology.org/search/?q=Yejin+Choi) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Yejin+Choi)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) 2

## Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

- **kept** — Partial-input controls for every course, after the paper's ablation: run the student with the lesson removed (question + options only) and with the question removed (lesson + options only). *([Cosmos QA: Machine Reading Comprehension with Contextual…](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/) · permanent course controls: no lesson, questions shuffled, key lines removed; all red in all seven courses (biology real 2238/4581, no lesson 1208, shuffled…)*

## Papers

1. **[StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://md.research.elamur.ai/free/en/papers/fisher-2024-styleremix-interpretable-authorship-obfuscation-distillation/)** *(2024)* Authorship obfuscation rewrites a text so that its author is hard to identify, while keeping the content and fluency.
2. **[COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)** *(2020)* `measured` The paper argues that hand-built commonsense knowledge graphs can never cover everything an NLP system will meet.
3. **[Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)** *(2019)* `1 idea kept` Cosmos QA has 35,588 four-option questions on 21,886 paragraphs from personal-narrative blogs, written by crowd workers to need commonsense «between the lines»: likely causes of events, what happened before or after, plausible facts about people and things, and counterfactuals («What would have happened if…?»).
4. **[Abductive Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)** *(2019)* The paper introduces abductive reasoning over narratives. Given two observations O1 and O2, a system must pick the more plausible explanation from two hypotheses (αNLI), or generate one (αNLG). The ART dataset has about 20k contexts taken from ROCStories and over 200k hypotheses, written by crowdworkers and then…
5. **[Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)** *(2017)* The paper applies sequence-to-sequence neural models to both AMR parsing and AMR generation. The main obstacle is data sparsity, so the authors pretrain on millions of unlabelled Gigaword sentences: a parser labels them, a generator is pretrained on those pairs, and then both are fine-tuned on the 16,833 gold pairs.…

## Co-authors (21)

- [Chandra Bhagavatula](https://md.research.elamur.ai/free/en/authors/chandra-bhagavatula/) ×3
- [Ronan Le Bras](https://md.research.elamur.ai/free/en/authors/ronan-le-bras/) ×3
- [Keisuke Sakaguchi](https://md.research.elamur.ai/free/en/authors/keisuke-sakaguchi/) ×2
- Antoine Bosselut
- Ari Holtzman
- [Chaitanya Malaviya](https://md.research.elamur.ai/free/en/authors/chaitanya-malaviya/)
- Doug Downey
- Hannah Rashkin
- Ioannis Konstas
- Jeff Da
- [Jena D. Hwang](https://md.research.elamur.ai/free/en/authors/jena-d-hwang/)
- Jillian Fisher
- Lifu Huang
- [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/)
- [Mark Yatskar](https://md.research.elamur.ai/free/en/authors/mark-yatskar/)
- Mitchell L Gordon
- Scott Wen-tau Yih
- Skyler Hallinan
- Srinivasan Iyer
- Ximing Lu
- Zaid Harchaoui

## More papers in the library (34)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [A Roadmap to Pluralistic Alignment](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/) — Taylor Sorensen, Jared Moore, Jillian Fisher, et al. · 2024
- [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/) — Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al. · 2024
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://md.research.elamur.ai/free/en/papers/zhao-2024-wildchat-1m-chatgpt-interaction-logs/) — Wenting Zhao, Xiang Ren, Jack Hessel, et al. · 2024
- [WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs](https://md.research.elamur.ai/free/en/papers/han-2024-wildguard-open-one-stop-moderation/) — Seungju Han, Kavel Rao, Allyson Ettinger, et al. · 2024
- [Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/) — Jaehun Jung, Peter West, Liwei Jiang, et al. · 2023
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://md.research.elamur.ai/free/en/papers/kim-2023-soda-million-scale-dialogue-distillation/) — Hyunwoo Kim, Jack Hessel, Liwei Jiang, et al. · 2023
- [Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) — Liunian Harold Li, Jack Hessel, Youngjae Yu, et al. · 2023
- [The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning](https://md.research.elamur.ai/free/en/papers/lin-2023-unlocking-spell-base-llms-rethinking/) — Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, et al. · 2023
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/) — Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al. · 2022
- [Generating Sequences by Learning to Self-Correct](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/) — Sean Welleck, Ximing Lu, Peter West, et al. · 2022
- [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/) — Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al. · 2022
- [It's not Rocket Science: Interpreting Figurative Language in Narratives](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-it-s-not-rocket-science/) — Tuhin Chakrabarty, Yejin Choi, Vered Shwartz · 2022
- [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/) — Jaehun Jung, Lianhui Qin, Sean Welleck, et al. · 2022
- [Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) — Ximing Lu, Sean Welleck, Jack Hessel, et al. · 2022
- [RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) — Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al. · 2022
- [Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/) — Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al. · 2022
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/) — Peter West, Chandra Bhagavatula, Jack Hessel, et al. · 2022
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/) — Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al. · 2022
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/) — Jack Hessel, Ari Holtzman, Maxwell Forbes, et al. · 2021
- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/) — Alisa Liu, Maarten Sap, Ximing Lu, et al. · 2021
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/) — Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al. · 2021
- [Surface Form Competition: Why the Highest Probability Answer Isn't Always Right](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/) — Ari Holtzman, Peter West, Vered Shwartz, et al. · 2021
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/) — Xiujun Li, Xi Yin, Chunyuan Li, et al. · 2020
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) — Samuel Gehman, Suchin Gururangan, Maarten Sap, et al. · 2020
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/) — Maarten Sap, Saadia Gabriel, Lianhui Qin, et al. · 2020
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) — Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al. · 2019
- [DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/) — Kai Sun, Dian Yu, Jianshu Chen, et al. · 2019
- [Defending Against Neural Fake News](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/) — Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al. · 2019
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/) — Aida Amini, Saadia Gabriel, Shanchuan Lin, et al. · 2019
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) — Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al. · 2019
- [SocialIQA: Commonsense Reasoning about Social Interactions](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/) — Maarten Sap, Hannah Rashkin, Derek Chen, et al. · 2019
- [The Risk of Racial Bias in Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/) — Maarten Sap, Dallas Card, Saadia Gabriel, et al. · 2019
- [Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/) — Ari Holtzman, Jan Buys, Maxwell Forbes, et al. · 2018
- [QuAC: Question Answering in Context](https://md.research.elamur.ai/free/en/papers/choi-2018-quac-question-answering-context/) — Eunsol Choi, He He, Mohit Iyyer, et al. · 2018