The model learns to assign a continuous quality score to reviews by training on a classification task rather than a direct regression problem.
Neural networks transform complex input data into a high-dimensional embedding space where it becomes linearly separable for classification.
Word2Vec creates semantic vector representations of words, which are then processed sequentially by Recurrent Neural Networks (RNNs) to understand context.
Recurrent neural networks are slow due to their sequential nature, lack general-purpose understanding, and struggle to process bidirectional context simultaneously.
The Transformer architecture uses self-attention to process text in parallel, enabling BERT to learn general, contextual word representations through masked language modeling.
A simplified character-level model called 'Artist BERT' demonstrates how self-attention works by predicting masked characters in artist names.
A Siamese network architecture processes pairs of reviews to determine which is better, implicitly learning a pointwise quality score for each individual review.
47:28Tanmay Bakshi
47:26WAD
25:19Carly Richmonds
38:24Håkan Silfvernagel
35:16Ankit Patel
56:46Jodie Burchell
1:05:44Chris Heilmann, Daniel Cranney, Raphael De Lio & Developer Advocate at Redis
57:52Rainer Stropek
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Jobs that call for the skills explored in this talk.
Warmwind
Warmwind
Squarepoint Capital
London, United Kingdom
Devoteam
