README.md

Sequence-To-Sequence Dialogue Bot for goal-oriented task

Dialogue agent is able to handle multi-domain data. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers.

We also used a new Stanford NLP Group released dataset of 3,031 dialogues [2] that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation.

Here is a simple example of interaction with a trained dialogue bot (can be downloaded with deeppavlov/download.py):

x_text:: Where's the nearest parking garage?
>> the nearest is dish parking at dish_parking_address. is that okay?
x_text:: Yes, please set directions via a route that avoids all heavy traffic if possible.
>> it looks like there is a road block being reported on the route but i will still find the quickest route to dish_parking_address.
x_text:: Thanks so much for your help.
>> you're welcome. have a good day. end_of_dialogue

x_text:: Will it be warm in Camarillo over the next 2 days?
>> no, it's not gon na be warm in camarillo over the next 2 days
x_text:: Thank you car!
>> you're welcome! end_of_dialogue

x_text:: What day is my lab appointment, and who all will be there?
>> your lab appointment is on lab_appointment_date at lab_appointment_time with lab_appointment_party.
x_text:: Thanks!
>> you're welcome.

Usage

Requirements

To use a seq2seq_go_bot model you should have a pretrained goal-oriented bot model

• config deeppavlov/configs/seq2seq_go_bot/bot_kvret_infer.json is recommended to be used in inference mode  
• config deeppavlov/configs/seq2seq_go_bot/bot_kvret.json is recommended to be used in train mode

Config parameters:

• name always equals to "seq2seq_go_bot"
• source_vocab — vocabulary of tokens from context (source) utterances
• target_vocab — vocabulary of tokens from response (target) utterances
• start_of_sequence_token — token corresponding to the start of sequence during decoding
• end_of_sequence_token — token corresponding to the end of sequence during decoding
• bow_encoder — one of bag-of-words encoders from deeppavlov.models.encoders.bow module
  • name — encoder name
  • other arguments specific to your encoder
• debug — whether to display debug output (defaults to false) (optional)
• network — reccurent network that handles encoder-decoder mechanism
  • name equals to "seq2seq_go_bot_nn"
  • learning_rate — learning rate during training
  • target_start_of_sequence_index — index of start_of_sequence_token in decoder vocabulary
  • target_end_of_sequence_index — index of end_of_sequence_token in decoder vocabulary
  • source_vocab_size — size of encoder token vocabulary (size of source_vocab is recommended)
  • target_vocab_size — size of decoder token vocabulary (size of target_vocab is recommended)
  • hidden_size — LSTM hidden state size equal for encoder and decoder

Usage example

• To infer from a pretrained model with config path equal to path/to/config.json:

from deeppavlov.core.commands.infer import build_model_from_config
from deeppavlov.core.common.file import read_json

CONFIG_PATH = 'path/to/config.json'
model = build_model_from_config(read_json(CONFIG_PATH))

utterance = ""
while utterance != 'exit':
    print(">> " + model([utterance])[0])
    utterance = input(':: ')

• To interact via command line use deeppavlov/deep.py script:

cd deeppavlov
python3 deep.py interact path/to/config.json

References

[1] A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset

[2] Mihail Eric, Lakshmi Krishnan, Francois Charette, and Christopher D. Manning, Key-Value Retrieval Networks for Task-Oriented Dialogue – 2017