216 citations · 514 across the 35 of their papers we have counts for
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As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages
Wietse de Vries, Malvina Nissim
Large generative language models have been very successful for English, but other languages lag behind, in part due to data and computational limitations. We propose a method that…
Datasets and Models for Authorship Attribution on Italian Personal Writings
Gaetana Ruggiero, Albert Gatt, Malvina Nissim
Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification o…
Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality
Elisa Bassignana, Malvina Nissim, Viviana Patti
As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, wh…
Personal-ITY: A Novel YouTube-based Corpus for Personality Prediction in Italian
Elisa Bassignana, Malvina Nissim, Viviana Patti
We present a novel corpus for personality prediction in Italian, containing a larger number of authors and a different genre compared to previously available resources. The corpus…
Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias
Marion Bartl, Malvina Nissim, Albert Gatt
Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously…
GePpeTto Carves Italian into a Language Model
Lorenzo De Mattei, Michele Cafagna, Felice Dell'Orletta +2
In the last few years, pre-trained neural architectures have provided impressive improvements across several NLP tasks. Still, generative language models are available mainly for E…
What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models
Wietse de Vries, Andreas van Cranenburgh, Malvina Nissim
Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To…