Natural Language Processing

DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages

We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.

Towards User-centric Interpretability of NLP Models

With the astounding advances of artificial intelligence in recent years, the field of interpretability research has emerged as a fundamental effort to ensure the development of robust AI systems aligned with human values. In this talk, two perspectives on AI interpretability will be presented alongside two case studies in natural language processing. The first study leverages behavioral data and probing tasks to study the perception and encoding of linguistic complexity in humans and language models. The second introduces a user-centric interpretability perspective for neural machine translation to improve post-editing productivity and enjoyability. The need for such application-driven approaches will be emphasized in light of current challenges in faithfully evaluating advances in this field of study.

IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation

We present IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian on more than 40 billion words, reaching state-of-the-art performance for most Italian conditional language generation tasks.

Empowering Human Translators via Interpretable Interactive Neural Machine Translation

Discussing the potential applications of interpretability research to the field of neural machine translation.

Characterizing Linguistic Complexity in Humans and Language Models

Presenting my work on studying different metrics of linguistic complexity and how they correlate with linguistic phenomena and learned representations in neural language models

Contrastive Language-Image Pre-training for the Italian Language

We present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs.

Contrastive Image-Text Pretraining for Italian

The first CLIP model pretrained on the Italian language.

Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

We developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years, in the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language.

That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models

This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how they are modeled in a neural language model (NLM), highlighting how linguistic information encoded in representations changes when the model learns to predict complexity.

Interpreting Neural Language Models for Linguistic Complexity Assessment

This thesis presents a model-driven study of multiple phenomena associated with linguistic complexity, and how those get encoded by neural language models' learned representations.