[email protected] AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations
This work describes a self-supervised data augmentation approach used to improve learning models' performances when only a moderate …
Italian Transformers Under the Linguistic Lens
We investigate whether and how using different architectures of probing models affects the performance of Italian transformers in …
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 …
ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
We present ETC-NLG, an approach leveraging topic modeling annotations to enable fully-unsupervised End-to-end Topic-Conditioned Natural …
