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项目作者： richardbaihe

项目描述 ：
Memory consolidation for Contextual SLU with Multi-task Framework
高级语言： Python
项目地址: git://github.com/richardbaihe/conslu.git
创建时间： 2018-10-21T05:48:17Z
项目社区：https://github.com/richardbaihe/conslu
开源协议：
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CON-SLU Pytorch

Pytorch implementation of contextual SLU models:

m2mnet

m2mnet

SDEN

Model

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

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

Multi domain dialogue

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

2. requirement

Environment

  1. python 3.6
  2. cuda 9.0
  3. pytorch 0.4

Packages

  1. pip install fuzzywuzzy
  2. pip install sklearn-crfsuite

3. Quick start

  1. ./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