The document discusses various natural language processing (NLP) applications used at a company including automated self-service answers, in-chat interactions, conversation analysis, and recommending saved responses. It also outlines the key components needed to implement NLP in production including building labeled datasets, monitoring models, serving models in production, and storing models. The overall system is designed to enable various NLP models to be plugged in and orchestrated with business logic for customer support applications.

1. Everything around the
NLP
@neal_lathia
London.AI
February 2021
(yes, lots of hiring happening)

3. NLP applications in
our award-winning
customer support

4. NLP in help article search
⬅ Search query
⬅ Help article search results

5. ⬅ Prompt for information
⬅ Predefined answer with a
direct call to action
⬅ Self-selected chat closure
⬅ The chat first turn (this could
be more than one message)
Automated self-service answers
and in-chat interactions (2019)

6. Financial Crime
Account Management
Finding the best help & prioritising tasks
with rules & NLP
Other domains

7. Recommending saved responses

8. ⬅ Customer first turn
⬅ Friendly goodbyes or new
issue?
⬅ Agent first turn, resolving the
issue
Conversation analysis

9. How do we enable all
of this to happen?

10. How to get NLP into production
Train a model Build the rest of the system

11. Everything around the NLP
Conversation
feature store
Labelled dataset
creation
Monitoring &
analysis
Serving infra &
orchestration
Data labelling
Model store

12. Conversation feature stores?

13. Conversation feature stores translate
between messy events & structured input

14. ⬅ Customer first turn
⬅ More turns
Labelled dataset creation?
Labels from elsewhere ➡

15. ⬅ Customer first turn
⬅ More turns
Labelled dataset creation structures datasets
for various types of experiments
Labels from elsewhere ➡
Customer first turn Labels
... ...
Customer first turn Agent first turn
... ...

16. Model store?

17. Our model store makes it easier to get
artifacts into production & gives us
insights about what we’re shipping
Lovely data

18. Serving infra & orchestration?
https://qconlondon.com/london2020/presentation/modern-banking-1500-microservices

19. Orchestrator
Classifier for A
Classifier for B
Classifier for C
Winner!
Rules that determine
eligibility for topic C
Classifier for D
Serving infra & orchestration enables us to
plug & play models, turn some off, and
surround them with business logic

20. Monitoring & analysis - we track our
models’ performance in the same place we
track our product’s performance

21. Data labelling enables us to create unique
datasets using our agent’s expertise

22. Everything around the NLP
Conversation
feature store
Labelled dataset
creation
Monitoring &
analysis
Serving infra &
orchestration
Data labelling
Model store

23. Thanks!
@neal_lathia
London.AI February 2021