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What I thought was cool from
Monzo’s Help Search Algorithm
2
• Aug-17: Monzo released their new
Help Screen
• It’s cool, but not infallible
• Cutting-edge ML techniques
• We can learn a lot!
• Here’s the link:
https://monzo.com/blog/2017/08/22/the-help-search-algorithm/
Overview
The Challenge
3
Text (unstructured data) can be vague and a lot of words
irrelevant:
1a. Bidirectional Recurrent Neural Networks (BRNNs)
4https://www.slideshare.net/SessionsEvents/hanie-sedghi-research-scientist-at-allen-institute-for-artificial-intelligence-at-mlconf-seattle-2017
• Like a standard NN, but hidden layers get a feed from output layer
• Looks at context of previous states and as well as future states
• Used in speech recognition, language modelling, translation etc.
• Loosely based on the visual attention mechanism
• Not all words in a sentence are equally important to the overall meaning
• Same for sentences in a body of text
1b. Attention Mechanism
5
6https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf
Hierarchical Attention Network (HAN)
Classification
of the text /
question
BRNN
1. Learns context for each word in
sentence (summarises whole sentence
centred around that word in “annotation”)
Attn
Mechanism
2. Picks out the words most important to
sentence meaning & puts in new vector
BRNN
Attn
Mechanism
3. Builds sentences out of important words;
Learns context for each sentence based on
sentences around it.
4. Identifies which sentences are important
to overall message meaning
Other cool thing: “Transfer Learning”
7

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What I Thought Was Cool From Monzo's Help Search Algorithm

  • 1. What I thought was cool from Monzo’s Help Search Algorithm
  • 2. 2 • Aug-17: Monzo released their new Help Screen • It’s cool, but not infallible • Cutting-edge ML techniques • We can learn a lot! • Here’s the link: https://monzo.com/blog/2017/08/22/the-help-search-algorithm/ Overview
  • 3. The Challenge 3 Text (unstructured data) can be vague and a lot of words irrelevant:
  • 4. 1a. Bidirectional Recurrent Neural Networks (BRNNs) 4https://www.slideshare.net/SessionsEvents/hanie-sedghi-research-scientist-at-allen-institute-for-artificial-intelligence-at-mlconf-seattle-2017 • Like a standard NN, but hidden layers get a feed from output layer • Looks at context of previous states and as well as future states • Used in speech recognition, language modelling, translation etc.
  • 5. • Loosely based on the visual attention mechanism • Not all words in a sentence are equally important to the overall meaning • Same for sentences in a body of text 1b. Attention Mechanism 5
  • 6. 6https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf Hierarchical Attention Network (HAN) Classification of the text / question BRNN 1. Learns context for each word in sentence (summarises whole sentence centred around that word in “annotation”) Attn Mechanism 2. Picks out the words most important to sentence meaning & puts in new vector BRNN Attn Mechanism 3. Builds sentences out of important words; Learns context for each sentence based on sentences around it. 4. Identifies which sentences are important to overall message meaning
  • 7. Other cool thing: “Transfer Learning” 7