SlideShare a Scribd company logo
1/20
2/20
Chorus: A Crowd-powered
Conversational Assistant
"Is there anything else I can help you with?":
Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
Ting-Hao K. Huang, Walter S. Lasecki, Amos Azaria, Jeffrey P. Bigham. HCOMP’16
3/20
Guardian: A Crowd-Powered Dialog System
for Web APIs
Guardian: A Crowd-Powered Spoken Dialog System for Web APIs
Ting-Hao K. Huang, Walter S. Lasecki, Jeffrey P. Bigham. HCOMP’15
4/20
Crowd-powered Entity Extraction
5/20
Time-Limited Output-Agreement Mechanism
Sunday flights from New York City to Las Vegas
Answer
Aggregate
Destination:
Las Vegas
RecruitedPlayers
Time Constraint
6/20
Worker Interface
http://tinyurl.com/DiaESPDemo
7/20
We Want to Know More!
How fast?
How many
players?
How good?
Trade-offs ?
8/20
3 Variables
Sunday flights from New York City to Las Vegas
Answer
Aggregate
Destination:
Las Vegas
RecruitedPlayers
Time Constraint
3. Answer Aggregate
Method
2. Time Constraint
1. Number of Players
9/20
Aggregate Method 1: ESP Only
ESP Answers
do NOT
Match
Empty
Label
ESP Answer
Matches
Time
10/20
Aggregate Method 2: 1st Only
ESP Answers
do NOT
Match
ESP Answer
Matches
Time
11/20
Aggregate Method 3: ESP + 1st
ESP Answers
do NOT
Match
ESP Answer
Matches
Time
12/20
Experiment
• Data
– Airline Travel Information System (ATIS)
• Class A: Context Independent
• Class D: Context Dependent
• Class X: Unevaluable
• Settings
– Focus on the toloc.city_name slot
– Number of workers = 10
– Time constraint = 15 and 20 seconds
– 3 aggregate methods
– Using Amazon Mechanical Turk
Simple
Complex
13/20
Simple Queries (Class A)
ESP + 1st
has the best quality
1st Only
has the best speed
20 seconds has
better quality &
similar speed
14/20
Trade-Offs on Simple Queries (Class A)
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10
Avg.ResponseTime(sec)
# Player
ESP + First (20 sec)
ESP + First (15 sec)
First (20 sec)
First (15 sec)
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
0 2 4 6 8 10
F1-score
# Player
ESP + 1st (20 sec)
ESP + 1st (15 sec)
1st (20 sec)
1st (15 sec)
0.65
0.70
0.75
0.80
0.85
0.90
0.95
5 6 7 8 9 10 11 12
F1-score
Avg. Response Time (sec)
10 Players
9 Player
8 players
7 Players
6 Players
5 Players
ESP + 1st
(20 sec)
1st Only
(20 sec)
More Players,
Faster
More Players,
Better Result
Faster,
Worse Result
15/20
0
1
2
3
4
5
6
7
8
9
1 2 3
Series1
Series2
0
0.2
0.4
0.6
0.8
1
1 2 3
Series1
Series2
Series3
F1-score Response Time (sec)
On Complex Queries (Class D & X)
Automatic
F1-score = 0.8
(Class D)
5 to 8 seconds
(1st / ESP+1st )
16/20
Now we know…
5 to 8 seconds.
10 Players!
(5 is also fine.)
F1 = 0.8 in Class D.
F1 = 0.9 in Class A.
Yes. Trade-offs.
17/20
Eatity System
• Extracting food entities from user messages
• Accuracy(Food) = 78.89% (In-lab study, 150 msgs)
Accuracy(Drink) = 83.33%
18/20
When to Use it?
• As a backup / support for automated annotators
– One player can be an automated annotator
– Low-confidence or failed cases / Validation
• Crowd-powered Systems
– Deployed Chorus: TalkingToTheCrowd.org
19/20
How about having humans do it?
20/20
Thank you!
@windx0303
Ting-Hao (Kenneth) Huang
Carnegie Mellon University
KennethHuang.cc
Jeffrey P. Bigham
Carnegie Mellon University
www.JeffreyBigham.com
Yun-Nung Chen
National Taiwan University
VivianChen.idv.tw
21/20
Backup Slides
22/20
How about having humans do it?
Ling Tung University, 35th 2016 Young Designers Exhibition, Taiwan
https://www.facebook.com/nownews/videos/10153864340447663/
23/20
Why always pick the 1st?
0.60
0.65
0.70
0.75
0.80
0.85
0 1 2 3 4 5 6 7
F1-score
Input Order (i)
10 Players
4 Players
2 Players
Because they are better.

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Real-time On-Demand Crowd-powered Entity Extraction

  • 2. 2/20 Chorus: A Crowd-powered Conversational Assistant "Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent Ting-Hao K. Huang, Walter S. Lasecki, Amos Azaria, Jeffrey P. Bigham. HCOMP’16
  • 3. 3/20 Guardian: A Crowd-Powered Dialog System for Web APIs Guardian: A Crowd-Powered Spoken Dialog System for Web APIs Ting-Hao K. Huang, Walter S. Lasecki, Jeffrey P. Bigham. HCOMP’15
  • 5. 5/20 Time-Limited Output-Agreement Mechanism Sunday flights from New York City to Las Vegas Answer Aggregate Destination: Las Vegas RecruitedPlayers Time Constraint
  • 7. 7/20 We Want to Know More! How fast? How many players? How good? Trade-offs ?
  • 8. 8/20 3 Variables Sunday flights from New York City to Las Vegas Answer Aggregate Destination: Las Vegas RecruitedPlayers Time Constraint 3. Answer Aggregate Method 2. Time Constraint 1. Number of Players
  • 9. 9/20 Aggregate Method 1: ESP Only ESP Answers do NOT Match Empty Label ESP Answer Matches Time
  • 10. 10/20 Aggregate Method 2: 1st Only ESP Answers do NOT Match ESP Answer Matches Time
  • 11. 11/20 Aggregate Method 3: ESP + 1st ESP Answers do NOT Match ESP Answer Matches Time
  • 12. 12/20 Experiment • Data – Airline Travel Information System (ATIS) • Class A: Context Independent • Class D: Context Dependent • Class X: Unevaluable • Settings – Focus on the toloc.city_name slot – Number of workers = 10 – Time constraint = 15 and 20 seconds – 3 aggregate methods – Using Amazon Mechanical Turk Simple Complex
  • 13. 13/20 Simple Queries (Class A) ESP + 1st has the best quality 1st Only has the best speed 20 seconds has better quality & similar speed
  • 14. 14/20 Trade-Offs on Simple Queries (Class A) 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 Avg.ResponseTime(sec) # Player ESP + First (20 sec) ESP + First (15 sec) First (20 sec) First (15 sec) 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 0 2 4 6 8 10 F1-score # Player ESP + 1st (20 sec) ESP + 1st (15 sec) 1st (20 sec) 1st (15 sec) 0.65 0.70 0.75 0.80 0.85 0.90 0.95 5 6 7 8 9 10 11 12 F1-score Avg. Response Time (sec) 10 Players 9 Player 8 players 7 Players 6 Players 5 Players ESP + 1st (20 sec) 1st Only (20 sec) More Players, Faster More Players, Better Result Faster, Worse Result
  • 15. 15/20 0 1 2 3 4 5 6 7 8 9 1 2 3 Series1 Series2 0 0.2 0.4 0.6 0.8 1 1 2 3 Series1 Series2 Series3 F1-score Response Time (sec) On Complex Queries (Class D & X) Automatic F1-score = 0.8 (Class D) 5 to 8 seconds (1st / ESP+1st )
  • 16. 16/20 Now we know… 5 to 8 seconds. 10 Players! (5 is also fine.) F1 = 0.8 in Class D. F1 = 0.9 in Class A. Yes. Trade-offs.
  • 17. 17/20 Eatity System • Extracting food entities from user messages • Accuracy(Food) = 78.89% (In-lab study, 150 msgs) Accuracy(Drink) = 83.33%
  • 18. 18/20 When to Use it? • As a backup / support for automated annotators – One player can be an automated annotator – Low-confidence or failed cases / Validation • Crowd-powered Systems – Deployed Chorus: TalkingToTheCrowd.org
  • 19. 19/20 How about having humans do it?
  • 20. 20/20 Thank you! @windx0303 Ting-Hao (Kenneth) Huang Carnegie Mellon University KennethHuang.cc Jeffrey P. Bigham Carnegie Mellon University www.JeffreyBigham.com Yun-Nung Chen National Taiwan University VivianChen.idv.tw
  • 22. 22/20 How about having humans do it? Ling Tung University, 35th 2016 Young Designers Exhibition, Taiwan https://www.facebook.com/nownews/videos/10153864340447663/
  • 23. 23/20 Why always pick the 1st? 0.60 0.65 0.70 0.75 0.80 0.85 0 1 2 3 4 5 6 7 F1-score Input Order (i) 10 Players 4 Players 2 Players Because they are better.