Skip to content

Pytorch Implementation of "Sequential Dialogue Context Modeling for Spoken Language Understanding( https://arxiv.org/abs/1705.03455 )"

Notifications You must be signed in to change notification settings

DSKSD/SDEN-Pytorch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SDEN-Pytorch

Pytorch implementation of Sequential Dialogue Context Modeling for Spoken Language Understanding

Model

Requirements

pytorch==0.4
nltk==3.5.1
sklearn_crfsuite

Run

python3 main.py

Data

I have modified Stanford Multi-turn dataset to fit this model. So it has some noise especially slot tags. It consists of three domain, Weather, Schedule, Navigate. I did dialogue recombination for multi-domain dialogue and modified its format to BIO.

sample

Single domain dialogue

User :  Will it be hot in Inglewood over the next few days?
BOT  :  It will be warm both Monday and Tuesday in Inglewood.
User :  Thank you very much.
BOT  :  You're welcome. Hope you have a great day.

Multi domain dialogue

User :  is it going to be raining this weekend
BOT  :  What city are you inquiring about?
User :  Alhambra please.
BOT  :  It will be raining on Saturday and hailing on Sunday in Alhambra.
User :  Thanks.
BOT  :  happy to help
User :  I need a gas station
BOT  :  I have one gas station listed. Want more info?
User :  What is the address?
BOT  :  76 is at 91 El Camino Real.
User :  Thank you!
BOT  :  You're welcome, stay safe.

Devset Result

Intent Detection : 0.9503091367071216 (Accuracy)

Slot Extraction

precision recall f1-score support
B-agenda 0.256 0.278 0.267 36
I-agenda 0.733 0.407 0.524 54
B-date 0.826 0.836 0.831 911
I-date 0.533 0.885 0.665 549
B-distance 0.624 0.674 0.648 487
I-distance 0.424 0.353 0.386 167
B-event 0.813 0.793 0.803 517
I-event 0.637 0.847 0.727 367
B-location 0.718 0.928 0.809 572
I-location 0.384 0.950 0.547 280
B-party 0.298 0.807 0.435 187
I-party 0.471 0.471 0.471 17
B-poi_type 0.790 0.738 0.763 534
I-poi_type 0.528 0.718 0.608 301
B-room 1.000 0.400 0.571 35
I-room 0.683 0.848 0.757 33
B-time 0.496 0.595 0.541 220
I-time 0.129 0.286 0.178 14
B-traffic_info 0.661 0.527 0.587 237
I-traffic_info 0.749 0.636 0.688 272
B-weather_attribute 0.904 0.877 0.890 546
I-weather_attribute 0.954 0.775 0.855 80
avg / total 0.683 0.775 0.712 6416

About

Pytorch Implementation of "Sequential Dialogue Context Modeling for Spoken Language Understanding( https://arxiv.org/abs/1705.03455 )"

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published