We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.
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.
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.
Discussing the potential applications of interpretability research to the field of neural machine translation.
Presenting my work on studying different metrics of linguistic complexity and how they correlate with linguistic phenomena and learned representations in neural language models
A semantic browser for SARS-CoV-2 and COVID-19 powered by neural language models.
An overview of the latest advances in the field of NLP, with a focus on neural models and language understanding.