Machine Learning Street Talk (MLST) #062 - Dr. Guy Emerson - Linguistics, Distributional Semantics
Feb 3, 2022
Dr. Guy Emerson, a computational linguist at Cambridge, shares insights into distributional semantics and truth-conditional semantics. The conversations delve into the challenges of representing meaning in machine learning, the importance of grounding language in real-world contexts, and the interplay between cognition and linguistics. Emerson critiques traditional linguistic models, emphasizing the need for flexible frameworks. The discussion also touches on Bayesian inference in language, examining how context influences meaning and the complexities of vocabulary like 'heap' and 'tall'.
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Firth's Focus
- Guy Emerson points out that many citing Firth and Harris on distributional semantics haven't read their work.
- Firth focused on analyzing poetry, not large language model training.
Data Size and Human Language Learning
- Guy Emerson argues that massive language models, trained on unrealistic data volumes, don't reflect human language learning.
- He emphasizes the importance of human-like data sizes for modeling human language acquisition.
Grounding Abstract Concepts
- Grounding connects linguistic meaning with real-world experience, crucial for understanding abstract concepts.
- Guy Emerson emphasizes the need for a theory explaining how abstract concepts emerge from concrete ones, even if indirectly.