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Yohan Jo

Yohan Jo has 3 papers in our library, in 2017–2026, most of them on Speech act theory, Dialogue systems, Reasoning; often with Carolyn Penstein Rosé, Michael Yoder.

3 papers in our library · 2017–2026

OpenAlex · h-index 1 · 44 works · 7 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 12 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9681

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Seoul National UniversitySeoul · South Korea2024–2026 · 39 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Dialogue systems · 1 workSpeech act theory · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences (2017 · 11 citations)
    The paper tackles unsupervised recognition of dialogue acts (DAs) in conversations. The authors propose CSM, an HMM-based generative model with two main additions. First, it filters content words. Each conversation receives a slowly changing background topic, drawn from topics shared across the corpus, which absorbs…
  2. ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding (2026 · 1 citation)
    Large Reasoning Models (LRMs) often overthink. They find a correct intermediate answer in their chain of thought, keep deliberating, and then replace it with a wrong one. The authors first measure this with Qwen3-4B-Thinking. They force the </think> delimiter at each sentence boundary and pick the best cut-off in…
  3. Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction (2026)
    Whether a message sways someone depends heavily on who that person is. Yet systems that judge persuasiveness have no principled way to use a person's earlier online activity. Park et al. study this on the ChangeMyView (CMV) subreddit. The task is to decide whether a reply earned a delta from the post's author, given…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Modeling Dialogue Acts with Content Word Filtering and Speaker PreferencesConference on Empirical Methods in Natural Language Processing (EMNLP)Yohan Jo, Michael Yoder, Hyeju Jang, et al.2017 · 11 citations
  2. KL for a KL: On-Policy Distillation with Control Variate Baseline ↗arXiv:2605.07865 · no summary yetMinjae Oh et al.2026 · 8 citations
  3. ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided DecodingFindings of the Association for Computational LinguisticsSangjun Song, Minjae Oh, Seungkyu Lee, et al.2026 · 1 citation
  4. Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness PredictionFindings of the Association for Computational LinguisticsSejun Park, Yoonah Park, Jongwon Lim, et al.2026

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 12 times (of the 149,439 papers whose bibliographies we read).

Updated

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