Tommaso Fornaciari

Tommaso Fornaciari has 3 papers in our library, in 2021–2023, most of them on Evaluation & benchmarks; often with Alexandra Uma, Barbara Plank, Massimo Poesio.

3 papers in our library · 2021–2023

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

Affiliations

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

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

The most cited first (OpenAlex and our library).

We Need to Consider Disagreement in Evaluation (2021 · 185 citations)
This position paper questions the usual NLP practice of scoring predictions against a single aggregated "gold standard." The authors argue that this assumption breaks down because many items allow several valid labels and because annotators often disagree.
Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning (2021 · 143 citations)
Supervised NLP models usually train against a single "correct" label per item, even though human annotators often disagree.
SemEval-2023 Task 11: Learning with Disagreements (LeWiDi) (2023 · 100 citations)
Annotators of subjective NLP tasks such as hate speech or offensiveness detection often disagree, and merging their labels into one "gold" answer misrepresents the data.

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

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

Papers of our library cite their works 259 times (of the 141,840 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated 24 October 2026