MilaNLP Lab

We are the Milan Natural Language Processing Group. We study, develop and deploy NLP algorithms to solve real-world problems.

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Meet the team!

Researchers

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Dirk Hovy

Associate Professor

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Debora Nozza

Assistant Professor

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Amanda Cercas Curry

Postdoc

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Giuseppe Attanasio

Postdoc

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Nikita Soni

Visiting PhD Student

Students

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Greta Damo

Master Student

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Nunzio Fallico

Master Student

Alumni

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Anne Lauscher

Associate Professor, University of Hamburg

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Federico Bianchi

Postdoc, Stanford University

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Tommaso Fornaciari

Direttore Tecnico Superiore della Polizia di Stato

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Kilian Theil

Researcher Associate, University of Mannheim

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Matthias Orlikowski

PhD Student, Bielefeld University

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Paul Röttger

PhD Student, University of Oxford

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Pietro Lesci

PhD student, Cambridge University

Projects

INTEGRATOR

Incorporating Demographic Factors into Natural Language Processing Models

Twitter Healthy Conversations

Devising Metrics for Assessing Echo Chambers, Incivility, and Intolerance on Twitter

Recent Twitter activity

Recent Publications

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Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

October, 2022
The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, …
PDF Project

Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

September, 2022
Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, …
PDF Project

HATE-ITA: Hate Speech Detection in Italian Social Media Text

July, 2022
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing …
PDF Code Poster Slides

Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

July, 2022
Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially …
PDF Code

Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

May, 2022
Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing …
PDF Code Project
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