Presented at CAIN22 - 1st Conference on AI Engineering – Software Engineering for AI Due to the migration megatrend, efficient and effective second- language acquisition is vital. One proposed solution involves AI- enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews. The action team elicited a set of 38 requirements for which we designed corresponding automated test cases for 15 of particular interest to the evolving solution. Our results show that six of the test case designs can detect meaningful differences between candidate models. While quality assurance of natural language processing applications is complex, we provide initial steps toward an automated framework for machine learning model selection in the context of an evolving conversational agent. Future work will focus on model selection in an MLOps setting.

1. Quality Assurance
Of
Generative Dialog Models
in an evolving
Conversational Agent
used for
Swedish Language Practice
May 16, 2022
Markus Borg, Johan Bengtsson,
Harald Österling, Alexander Hagelborn,
Isabella Gagner, Piotr Tomaszewski

2. Markus
Borg
Isabella
Gagner
Alexander
Hagelborn
Harald
Österling
Piotr
Tomaszewski
Johan
Bengtsson

3. Fundamental AI
Applied AI
Operational AI

4. 4

5. Motivation
and Context

7. Generative Dialog Model (GDM)
• produces human-like replies to user prompts
BlenderBot
• Facebook’s pretrained open domain GDM
• Emely is transfer-learned from BlenderBot for job interviews
Application-
specific
data

8. Swedish
Learner
UI
Speak
Speech to
Text
GDM
Eng-Swe
Translation
(I)
Text to
Speech
Listen
Toxic
Filter
Swe-Eng
Translation
Reject input
Emely Architecture
Component under Test

9. (Thompson et al., Translational Psychiatry, 10(1). 2020)

10. RQ1: What are the requirements on Emely's GDM?
RQ2: How can test cases be designed to verify them?
RQ3: How to automate them for the evolution of Emely?

11. Method

12. “a disciplined process of
inquiry conducted by and
for those taking the action”
12

13. 13
• Not participatory: Case study research
Alternatives for industry-academia collaboration
• Design Science Methodology = acquire knowledge through the design of artifacts
• Tech Transfer Research Methodology = handover of research results to industry
• Action Research = make a change in a socio-technical system

14. Standup meetings
Sprint planning
Shared Slack workspace
GitHub repo
Academic Researcher
AI Developer
Problem Owner
1. Diagnosing
2. Action
Planning
3. Action
Taking
4. Evaluating
5. Learning
Action Research Cycles
RQs

15. Results

16. RQ1 – Requirements Engineering
1. Elicitation
• Interviews
• Market analysis
2. Specification
3. Analysis
• Organization
• Prioritization
4. Validation
We selected 15 for
test case design

17. RQ1 – Requirements Engineering
The GDM shall …
… provide replies with low n-gram repeatability.
answering
… provide coherent replies with respect to the ongoing dialog.
… be robust against prompts with incorrect word order.
intelligence
understanding

18. RQ2 – Test Design
The GDM shall …
… provide replies with low n-gram repeatability.
… provide coherent replies with respect to the ongoing dialog.
… be robust against prompts with incorrect word order.
answering
intelligence
understanding
Simple check – Count the n-grams
Pretrained sentence-BERT model for ”next sentence prediction”
Provide information… wait… Request the same information
Repeat the process with randomly swapped words

19. RQ3 – Test Implementation
(A) Blenderbot
(B) GDM
Under Test
(C)
Controlled
Test Data
p
Logs
Generation (D)
Analysis
• Emely v0.2, v0.3, v0.4
• BlenderBot

20. RQ3 – Test Implementation
answering
… provide replies with
low n-gram repeatability.
Repetition
metric

21. RQ3 – Test Implementation
… provide coherent replies with respect to the ongoing dialog.
intelligence
Dialogs with at least one reply that BERT considers random
Emely v02 – 122
Emely v03 – 79
Emely v04 – 71
BlenderBot – 114

22. RQ3 – Test Implementation
… be robust against prompts with incorrect word order.
understanding
None of the GDMs
even passed basic
memory tests…

23. Implications for
Research and
Practice

24. Requirements on
GDMs that can be
resused

26. markus.borg@ri.se
@mrksbrg
mrksbrg.com
RISE Research Institutes of Sweden
AI quality assurance
needs to be tackled from
two directions
- aligned through
pipeline automation