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47 The rise of end-to-end learning

Suppose you want to build a system to examine online product reviews and automatically tell
you if the writer liked or disliked that product. For example, you hope to recognize the

following review as highly positive:

This is a great mop!

and the following as highly negative:

This mop is low quality--I regret buying it.
The problem of recognizing positive vs. negative opinions is called “sentiment classification.”

To build this system, you might build a “pipeline” of two components:

1. Parser: A system that annotates the text with information identifying the most
15
important words. For example, you might use the parser to label all the adjectives
and nouns. You would therefore get the following annotated text:

This is a great​ mop​ !
Adjective​ Noun​
2. Sentiment classifier: A learning algorithm that takes as input the annotated text and
predicts the overall sentiment. The parser’s annotation could help this learning
algorithm greatly: By giving adjectives a higher weight, your algorithm will be able to
quickly hone in on the important words such as “great,” and ignore less important
words such as “this.”

We can visualize your “pipeline” of two components as follows:

There has been a recent trend toward replacing pipeline systems with a single learning
algorithm. An ​end-to-end learning algorithm​ for this task would simply take as input
the raw, original text “This is a great mop!”, and try to directly recognize the sentiment:

15 A parser gives a much richer annotation of the text than this, but this simplified description will
suffice for explaining end-to-end deep learning.

Page 91 Machine Learning Yearning-Draft Andrew Ng