Valerio Basile presents an interesting consideration about the difference between form and meaning of language in neural language models.
Title: Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
The success of the large neural language models on many NLP tasks is exciting. However, these successes sometimes lead to hype in which these models are being described as “understanding” language or capturing “meaning”. In this position paper it is argued that a system trained only on form has a priori no way to learn meaning, and that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.
Emily M. Bender and Alexander Koller make their point through an incredibly witty story involving a very curious sea creature and a couple of castaways on bear-ridden tropical islands.
Related Paper: Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
When: On 7th May at 11.30 am
Where: https://unito.webex.com/webappng/sites/unito/meeting/info/910eaf7ad0534d1ba92c5dde0a66a9a7