CON-SLU Pytorch

Pytorch implementation of contextual SLU models:

m2mnet

SDEN

1.Data

kvret

M2M_Simulated Dialogue

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.

2. requirement

Environment

python 3.6
cuda 9.0
pytorch 0.4

Packages

pip install fuzzywuzzy
pip install sklearn-crfsuite

3. Quick start

./train.sh 0 context_s2s

the first param 0 means choose GPU:0 for this model training process, and context_s2s is the model we want to train.

In this repository, we support s2s, sden, memnet, context_s2s four models.

4. Devset Result

Intent Detection : 0.93804 (Accuracy)

Slot Extraction

	precision	recall	f1-score	support
B-address	0	0	0	3
I-address	0	0	0	6
B-agenda	0	0	0	3
I-agenda	0.8	0.5	0.615	8
B-date	0.774	0.828	0.8	145
I-date	0.076	0.854	0.139	103
B-distance	0.693	0.859	0.767	92
I-distance	0.361	0.393	0.376	56
B-event	0.891	0.942	0.916	104
I-event	0.932	0.75	0.831	92
B-location	0.952	0.98	0.966	101
I-location	0.811	0.977	0.887	44
B-party	0.931	0.9	0.915	30
I-party	1	0.571	0.727	7
B-poi	0.692	0.562	0.621	32
I-poi	0.006	0.111	0.011	18
B-poi_type	0.831	0.852	0.841	81
I-poi_type	0.407	1	0.578	50
B-room	0	0	0	1
I-room	0	0	0	1
B-time	0.975	0.83	0.897	47
I-time	0.933	0.933	0.933	45
B-traffic_info	0.622	0.5	0.554	46
I-traffic_info	0.679	0.581	0.626	62
B-weather_attribute	0.939	0.951	0.945	81
I-weather_attribute	0.818	1	0.9	9
avg / total	0.716	0.803	0.725	1267