This document discusses improving the interpretability of RASA NLU models through machine learning techniques. It introduces interpretable machine learning and how tools like ScatterText and LIME can be used to analyze RASA NLU training data and models. These techniques help identify confusing intents, common words between intents, and explain model predictions. The goal is to troubleshoot models and refine training data to improve natural language understanding.

1. Improving RASA NLU Training
Using Machine Learning
Interpretability Techniques
Zvi Topol, MuyVentive LLC

2. Background
š Data Scientist with experience in various verticals – media and entertainment, marketing
analytics, industrial IOT
š Started MuyVentive, LLC to work on Data Science and Interpretable Machine Learning
Tools with Focus on Conversational Interfaces
š Also offering Data Science consulting for startups

3. What is this Talk about?
Visualization
Interpretable
Machine
Learning
NLU
š Introduction to the topic of interpretable ML and how it can be applied to Rasa NLU using
open-source software

4. Interpretable Machine Learning
š Very active research topic
š Different audiences and requirements for interpretability
š Interpreting classifiers
š Decision trees/decision lists (IF-THEN statements) (simple models, e.g. rule lists, decision trees)
š Black box method explanations – local vs. global (Examples - LIME, Anchors, Shapley, decision
trees, PDP. Influence functions)
š Training and Test Set Analysis
š Finding conflicts/commonalities between classes (Example – ScatterText)
š “diverse” training sets

5. Common Stack for Conversational
Interfaces
NLU (RASA NLU)
NLG
Dialog Management (RASA Core)
TTS
STT

6. Common Natural Language
Understanding Concepts
“What is the balance in my checking account?”
Intent:
PersonalAccountsIntent
Entities
“Whom can I speak with regarding mortgages?”
Entities
Intent: OtherServicesIntent

7. Typical Training of NLU Models
š Identify Intents and entities
š Provide multiple examples for each intent with labeled entities
š Train machine learning algorithms for intent classification and entity recognition
š Rasa also supports multi-intents
š This talk – focus on single intents

8. Scoring the NLU Model
{
"intent": { "name": "other_services_intent", "confidence": 0.8726195067816652
},
"entities": [],
"intent_ranking": [ { "name": "other_services_intent", "confidence":
0.8726195067816652 },
{ "name": "personal_accounts_intent", "confidence":
0.12738049321833478 } ],
"text": "what mortgage rate do you offer for 30 yr loans?",
"project": "nlu",
"model": "model_20181101-071533”
}

9. Common Approaches For
Troubleshooting Operationalized NLU
Models
š Monitor performance of model, identify misclassified examples with top confidence score
as candidates to refine and retrain
š Use Active Learning to intelligently pick which non-labeled samples to label
š How to identify (confusing) similar intents with different labels?

10. ScatterText
š Project by Jason Kessler with focus on visualizing differences between textual corpuses by
word frequencies
š https://github.com/JasonKessler/scattertext
š Can be leveraged to identify words which are unique for each intent and common across
intents

11. ScatterText Example

12. ScatterText - Some Considerations
š Scalability
š Binary
š Needs enough examples
š Supports unigrams only
š Synonyms/deeper semantic simalarities

13. LIME
š Local Interpretable Model-Agnostic Explanations
š Explaining classification for text and image data as well as regression models by getting as
an input:
š A model
š An input example X with label and confidence score
š Sampling examples in the local vicinity of X to fit a simple interpretable model
š Use the model to explain the global model

14. LIME Example

15. LIME For NLU
Without the word ”annual”
š { "intent": { "name":
"personal_accounts_intent",
"confidence": 0.5760200117371109 },
"entities": [], "intent_ranking": [ {
"name": "personal_accounts_intent",
"confidence": 0.5760200117371109 }, {
"name": "other_services_intent",
"confidence": 0.42397998826288913 }
], "text": "what are rates for my savings
accounts", "project": "nlu", "model":
"model_20181030-064347" }
Original
š { "intent": { "name":
"other_services_intent", "confidence":
0.5537862252760863 }, "entities": [],
"intent_ranking": [ { "name":
"other_services_intent", "confidence":
0.5537862252760863 }, { "name":
"personal_accounts_intent",
"confidence": 0.44621377472391366 }
], "text": "what are annual rates for my
savings accounts", "project": "nlu",
"model": "model_20181030-064347" }

16. For More Information
š There is a lot more to interpretable ML and conversational analytics
š MuyVentive blog - http://www.muyventive.com/blog.html
š zvi.topol@muyventive.com