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Silvia Terragni

Silvia Terragni has 2 papers in our library, in 2021, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Dirk Hovy, Federico Bianchi, Debora Nozza.

2 papers in our library · 2021

OpenAlex · ORCID · h-index 8 · 19 works · 692 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 158 times in our library · ⭐ Paper Stars ranking: #3111

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

🎓University of Milano-BicoccaMilan · Italy2019–2023 · 13 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.

 Concepts, shelves and topics

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksGeneral Social Sciences · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20212 papersSilvia Terragni's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021 · 366 citations)
    Classic topic models take bag-of-words (BoW) input, which ignores word order and meaning. The resulting topics are often hard to interpret. The authors ask whether pre-trained contextual representations can make topics more coherent.
  2. Cross-lingual Contextualized Topic Models with Zero-shot Learning (2021 · 215 citations)
    Classic bag-of-words topic models are bound to the vocabulary of their training language. They cannot process unseen words, and they cannot be reused for texts in other languages. Bianchi et al. propose ZeroShotTM, a neural topic model built on Neural-ProdLDA (a VAE). It takes multilingual SBERT document embeddings…

 Co-authors here

4 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 158 times (of the 141,840 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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