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Yi-Ling Chung

Yi-Ling Chung has 2 papers in our library, in 2019–2020, most of them on Sentiment & emotion, Bias, fairness & safety, Speech & spoken language; often with Marco Guerini, Serra Sinem Tekiroglu.

2 papers in our library · 2019–2020

OpenAlex · ORCID · h-index 6 · 25 works · 181 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 187 citations in our library · ⭐ Paper Star rank #3901

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

🔬The Alan Turing InstituteLondon · United Kingdom2023–2025 · 7 paperscurrent🔬Fondazione Bruno KesslerTrento · Italy2018–2021 · 4 papers🎓University of TrentoTrento · Italy2021 · 2 papers🎓University of the Basque CountryLeioa · Spain2018–2021 · 2 papers

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

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Computer Science · 2 works Social Sciences · 2 works Artificial Intelligence · 2 works Sociology and Political Science · 2 works Safety Research · 1 work

🧭 Topics 4 Bias, fairness & safety · 2 works Sentiment & emotion · 2 works Multilingual & cross-lingual NLP · 1 work Speech & spoken language · 1 work

 Works by year

2020s

20201 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019 · 209 citations)
    Online hate is usually handled by deleting posts or suspending accounts, which can be criticized as censorship.
  2. Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020 · 153 citations)
    Counter-narratives (CNs) are calm, fact-based replies to hate speech (HS). They are an alternative to deleting content, but expert-written CNs are slow to produce and too few to train generation models. The authors first compare three existing ways of collecting HS-CN pairs. Crawling from social media (CRAWL) yields…

 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 187 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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