The document summarizes the Fourth Dialog State Tracking Challenge (DSTC4) held at IWSDS 2016. It provides an overview of the main task, which involved dialogue state tracking on a dataset of tourist information dialogues in Singapore. Participants were asked to estimate dialogue states across turns to track topics, slots, and values. The challenge evaluated submissions using frame accuracy and slot-based metrics. Ensemble methods that combined top-performing systems, such as majority voting of the top 3 entries, achieved the best results.

1. IWSDS 2016
The Fourth Dialog State Tracking
Challenge (DSTC4)
Seokhwan Kim1, Luis Fernando D’Haro1, Rafael E. Banchs1,
Jason D. Williams2, Matthew Henderson3
1 Institute for Infocomm Research, 2 Microsoft Research, 3 Google

2. IWSDS 2016
Dialogue State Tracking
• A key subtask in dialogue management
• To estimate the user’s goal as a dialogue progresses
Pg 2
Utterance Food Area
S Hello, How may I help you? Persian South
U I need a Persian restaurant in the south part of town.
S What kind of food would you like? Persian South
U Persian.
S I’m sorry but there is no restaurant serving persian food Portuguese South
U How about Portuguese food?
S Peking restaurant is a nice place in the south of town. Portuguese South
U Is that Portuguese?
S Nandos is a nice place in the south of town serving tasty Portuguese food. Portuguese South
U Alright. Whats the phone number?
S The phone number of nandos is 01223 327908 . Portuguese South
U And the address?
S Sure, nandos is on Cambridge Leisure Park Clifton Way. Portuguese South
U Thank you good bye.

3. IWSDS 2016
Previous Dialog State Tracking Challenges
• DSTC1 (Williams et al., SIGDIAL 2013)
– Human-machine dialogues on bus timetable search
– Collected with Let’s go (CMU)
– Focused on the evaluation metrics for state tracking
• DSTC2 (Henderson et al., SIGDIAL 2014)
– Human-machine dialogues on restaurant search
– Collected with Cambridge University’s system
– Introduced changing user goals in a single dialogue session
• DSTC3 (Henderson et al., IEEE SLT 2014)
– Human-machine dialogues on tourist information search
– Collected with Cambridge University’s system
– Addressed the problem of adaptation to a new domain from DSTC2
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4. IWSDS 2016
TourSG: Dataset for DSTC4
• Human-human dialogues
• Tourist information in Singapore
• Speakers
– Guide (3 actual tour guides from Singapore)
– Tourist (35 possible tourists from Philippines)
• Characteristics
– Goal-oriented dialogues
– Mixed-initiative dialogues
– Knowledge-based dialogues
– Multi-topic dialogues
– Verbose dialogues
– Noisy dialogues
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5. IWSDS 2016
DSTC4: Timeline
Period Task
Mar 2012 –
Oct 2012
Data collection and annotation
Sep 2014 –
Dec 2014
Internal discussions
7 Dec 2014 Challenge planning meeting @ SLT 2014
Dec 2015 –
Apr 2015
Labelling additional annotations and building resources for evaluation
15 Apr 2015 –
16 Aug 2015
Development phase of the main and pilot tasks of DSTC4
17 Aug 2015 –
31 Aug 2015
Evaluation phase of the main task of DSTC4
14 Sep 2015 –
16 Sep 2015
Evaluation phase of the pilot tasks of DSTC4
30 Sep 2015 Paper submission deadline to IWSDS 2016
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6. IWSDS 2016
Main Task: Dialogue State Tracking
• Motivation
– Each subject could be expressed through a series of multiple turns
– Multiple topics are interlaced in a session
• Problem Definition
– Dialogue state tracking for each sub-dialogue level
– Focusing on the most common topic categories
• Annotations
– Segmentation
– Topic Category
– Frame Structure for major topic categories
• Itinerary, Accommodation, Attraction, Food, Transportation
Pg 6

7. IWSDS 2016
Examples of Dialogue States
Pg 7
Tourist
Can you give me some uh- tell me some cheap rate hotels, because I'm planning just to leave my
bags there and go somewhere take some pictures.
Guide Okay. I'm going to recommend firstly you want to have a backpack type of hotel, right?
Tourist
Yes. I'm just gonna bring my backpack and my buddy with me. So I'm kinda looking for a hotel that
is not that expensive. Just gonna leave our things there and, you know, stay out the whole day.
Guide
Okay. Let me get you hm hm. So you don't mind if it's a bit uh not so roomy like hotel because you
just back to sleep.
Tourist Yes. Yes. As we just gonna put our things there and then go out to take some pictures.
Guide Okay, um-
Tourist Hm.
Guide Let's try this one, okay?
Tourist Okay.
Guide
It’s InnCrowd Backpackers Hostel in Singapore. If you take a dorm bed per person only twenty
dollars. If you take a room, it's two single beds at fifty nine dollars.
Tourist Um. Wow, that's good.
Guide
Yah, the prices are based on per person per bed or dorm. But this one is room. So it should be fifty
nine for the two room. So you're actually paying about ten dollars more per person only.
Tourist Oh okay. That's- the price is reasonable actually. It's good.
TOPIC ACCOMMODATION
TYPE Hostel
PRICERANGE Cheap
TOPIC ACCOMMODATION
NAME InnCrowd
Backparkers Hostel

8. IWSDS 2016
Examples of Dialogue States
Pg 8
Tourist So uh is it near the airport?
Guide
Hm no. But you can get there easily by taking the trains from the airport. You just need to make a
change in the train direction.
Tourist
Hm okay. Because I have no idea at all about Singapore trains or transit. Uh how can I go to the
train or to the transit from the airport? Is it just outside the airport?
Guide So when you reach the airport you go down to the basement.
Tourist Um. Okay.
Guide
So you get your ticket, you pay your deposit. And I think at the airport they gave you a map. and- to
give you an idea. So all this is free. And then you travel along the East line towards the West. Can
you see Tanah Merah on the second stop?
Tourist Okay. Hm, Tanah Merah, yes.
Guide
Okay. So that is where you change to go down to town to the West towards the West. And you go
down to- I think the easiest way is to go to Outram Park.
Tourist Outrum Park.
Guide Yah.
Tourist Alright.
Guide
So when you get up there, you take the line towards Little India. So it's one, two, three stops and
you are at Little India.
Tourist Hm, okay.
TOPIC TRANSPORTATION
FROM Changi Airport
TO InnCrowd
Backparkers Hostel
BY MRT

9. IWSDS 2016
Examples of Dialogue States
Pg 9
Guide
So this is the place that you can go out and try street food. you can soak in the atmosphere. You
would love taking your camera out because you can photograph the Indian garland makers, the
fortune tellers. Uh it's full of life and culture. It's one of my favourite places.
Tourist Oh- Oh, great. Oh Yah. Is Little India is like a Indian community town or Indiantown?
Guide
Yes. So there are Hindu temples there. You can photograph beautiful architecture and statues of
the different Deities, the Hindu Deities.
Tourist
Uh huh. Okay. Great. So other than Indiantowns, are there other uh nations town there or race
town? What else?
Guide
Okay. And then Chinatown you take the same line. Two, three stops down. So you'll get off at
Chinatown, you are right in the heart of Chinatown. And in Chinatown we have uh also Bhuddist
temple and Terrace temple also great for photography.
Tourist
Uh Yes. Yes, okay. Okay, great. So we have Little India, then Chinatown. Other than that two, there
are other kinds of town, right? Like uh- is there a um something like Vietnamese town or just the
two of these?
Guide
Not Vietnamese but there is uh Kampong Glam which you have to go by bus because- well actually
you could go by train because you are young and healthy you can walk.
Tourist Hm. Yah, I like walking.
TOPIC ATTRACTION
NAME Little India
TOPIC ATTRACTION
TYPE Ethnic enclave
TOPIC ATTRACTION
NAME Chinatown
TOPIC ATTRACTION
TYPE Ethnic enclave
TOPIC ATTRACTION
NAME Kampong Glam

10. IWSDS 2016
Examples of Dialogue States
Pg 10
Tourist
So what about other than street food, of course I have to eat my dinner. Wha~ where do you
suggest me to eat my dinner? I also want to experience Singaporean delicacies or Singaporean
dishes.
Guide do you like hot food? Do you like curries?
Tourist Curries?
Guide Yah.
Tourist
Indian curries? What about Singaporean restaurants? Like they, you know, they offer Singaporean
delicacies or Singaporean dishes? Do you have a Singaporean dishes in Singapore?
Guide
Uh, Singaporean food is mostly try at the uh food courts. This is one I am recommending to you. It's
at old market. It's Maxwell Road Food Centre.
Tourist Um. Road Food Centre.
Guide
So it is at place called Maxwell Road which is in Chinatown. So if you take the train to Chinatown
from where you are and you'd- It's near. You just walk there.
Tourist Okay, nice.
TOPIC FOOD
CUISINE Singaporean
TOPIC FOOD
DISH Curry
TOPIC FOOD
CUISINE Singaporean
TOPIC FOOD
TYPE_OF_PLACE Hawker centre
NAME Maxwell Road Food Centre

11. IWSDS 2016
Main Task: Evaluation
• Resources
– Data
• Training set: 14 dialogues with 12,759 utterances
• Development set: 6 dialogues with 4,812 utterances
• Test set: 9 dialogues with 7,848 utterances
– Ontology
– Evaluation scripts
– Baseline tracker
• Fuzzy string matching with the ontology entries
– CodaLab: Web-based Competition Platform
• Metrics
– Schedules
• Schedule 1: all turns are included
• Schedule 2: only the turns at the end of segments are included
– Metrics
• Frame Structure-level Accuracy
• Slot-level Precision/Recall/F-measure
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12. IWSDS 2016
Pg 12
Schedule 1 Schedule 2
Team Entry Accuracy Precision Recall F-measure Accuracy Precision Recall F-measure
Baseline 0 0 0.0374 0.3589 0.1925 0.2506 0.0488 0.3750 0.2519 0.3014
1 0 0.0456 0.3876 0.3344 0.3591 0.0584 0.4384 0.3377 0.3815
1 0.0374 0.4214 0.2762 0.3336 0.0584 0.4384 0.3377 0.3815
2 0.0372 0.4173 0.2767 0.3328 0.0575 0.4362 0.3377 0.3807
3 0.0371 0.4179 0.2804 0.3356 0.0584 0.4384 0.3426 0.3846
2 0 0.0487 0.4079 0.2626 0.3195 0.0671 0.4280 0.3257 0.3699
1 0.0467 0.4481 0.2655 0.3335 0.0671 0.4674 0.3275 0.3851
2 0.0478 0.4523 0.2623 0.3320 0.0706 0.4679 0.3226 0.3819
3 0.0489 0.4440 0.2703 0.3361 0.0697 0.4634 0.3335 0.3878
3 0 0.1212 0.5393 0.4980 0.5178 0.1500 0.5569 0.5808 0.5686
1 0.1210 0.5449 0.4964 0.5196 0.1500 0.5619 0.5787 0.5702
2 0.1092 0.5304 0.5031 0.5164 0.1316 0.5437 0.5875 0.5648
3 0.1183 0.5780 0.4904 0.5306 0.1473 0.5898 0.5678 0.5786
4 0 0.0887 0.5280 0.3595 0.4278 0.1072 0.5354 0.4273 0.4753
1 0.0910 0.5314 0.3122 0.3933 0.1055 0.5325 0.3623 0.4312
2 0.1009 0.5583 0.3698 0.4449 0.1264 0.5666 0.4455 0.4988
3 0.1002 0.5545 0.3760 0.4481 0.1212 0.5642 0.4540 0.5031
5 0 0.0309 0.2980 0.2559 0.2754 0.0392 0.3344 0.2547 0.2892
1 0.0268 0.3405 0.2014 0.2531 0.0401 0.3584 0.2632 0.3035
2 0.0309 0.3039 0.2659 0.2836 0.0392 0.3398 0.2639 0.2971
6 0 0.0421 0.4175 0.2142 0.2831 0.0541 0.4380 0.2656 0.3307
1 0.0478 0.5516 0.2180 0.3125 0.0654 0.5857 0.2702 0.3698
2 0.0486 0.5623 0.2314 0.3279 0.0645 0.5941 0.2850 0.3852
7 0 0.0286 0.2768 0.1826 0.2200 0.0323 0.3054 0.2410 0.2694
1 0.0044 0.0085 0.0629 0.0150 0.0061 0.0109 0.0840 0.0194

13. IWSDS 2016
Main Task: Results
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14. IWSDS 2016
Main Tasks: Error Distribution
Pg 14

15. IWSDS 2016
Main Tasks: Ensemble Learning
Schedule 1 Schedule 2
Accuracy F-measure Accuracy F-measure
Single best entry 0.1212 0.5306 0.1500 0.5786
Top 3 entries: union 0.1111- 0.5147- 0.1325- 0.5619-
Top 3 entries: intersection 0.1241+ 0.5344+ 0.1561+ 0.5861+
Top 3 entries: majority voting 0.1172- 0.5194- 0.1421- 0.5703
Top 5 entries: union 0.0980- 0.5133- 0.1107- 0.5543-
Top 5 entries: intersection 0.1157 0.4370- 0.1369 0.5008-
Top 5 entries: majority voting 0.1183- 0.5210- 0.1439 0.5711
Top 10 entries: union 0.0623- 0.4719- 0.0680- 0.5014-
Top 10 entries: intersection 0.0300- 0.1816- 0.0453- 0.2275-
Top 10 entries: majority voting 0.1268+ 0.4741- 0.1456 0.5380-
All entries: union 0.0077- 0.1320- 0.0078- 0.1366-
All entries: intersection 0.0132- 0.0229- 0.0192- 0.0331-
All entries: majority voting 0.0646- 0.3535- 0.0898- 0.4135-
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16. IWSDS 2016
Pilot Tasks: Evaluation
• Tasks
– Spoken Language Understanding (SLU)
– Speech Act Prediction (SAP)
– Spoken Language Generation (SLG)
– End-to-end System (EES)
• Evaluation Metrics
– SLU and SAP
• Precision/Recall/F-measure
– SLG and EES
• BLEU
• AM-FM
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17. IWSDS 2016
• Resources
– Data
• Training set: 14 dialogues with 12,759 utterances
• Development set: 6 dialogues with 4,812 utterances
• Test set: 6 dialogues with 5,615 utterances
– Ontology
– Evaluation Scripts
• Offline evaluation
• Web-based evaluation
• Web-based Evaluation
Pilot Tasks: Evaluation
Pg 17
JSON Messages
Web-server
System
Participant
Web-client
Evaluation
Script
Organizer

18. IWSDS 2016
Pilot Tasks: Results
• Participant
– SLU
• Team 3 (5 entries)
• Results
Pg 18
Speech Act Semantic Tag
Speaker Entry Precision Recall F-measure Precision Recall F-measure
Guide 1 0.6287 0.5191 0.5687 0.5646 0.4886 0.5239
2 0.6330 0.5227 0.5726 0.5646 0.4886 0.5239
3 0.7451 0.6153 0.6740 0.5646 0.4886 0.5239
4 0.6314 0.5214 0.5712 0.5646 0.4886 0.5239
5 0.6762 0.5584 0.6117 0.5646 0.4886 0.5239
Tourist 1 0.3583 0.2977 0.3252 0.5741 0.4764 0.5207
2 0.2931 0.2435 0.2660 0.5741 0.4764 0.5207
3 0.5627 0.4675 0.5107 0.5741 0.4764 0.5207
4 0.2939 0.2442 0.2668 0.5741 0.4764 0.5207
5 0.5736 0.4766 0.5206 0.5741 0.4764 0.5207

19. IWSDS 2016
Conclusions
• DSTC4
– Main Task: Dialogue State Tracking
• Multi-topic, Mixed-initiative, Human-human conversations
• Tracking sub-dialogue segment-level state structures
• 24 entries from 7 participants
– Pilot Tasks
• SLU, SAP, SLG, EES
• Web-based evaluation
• 5 SLU entries from a participant
Pg 19

20. Thank You
Pg 20