Sequence-To-Sequence Dialogue Bot For Goal-Oriented Task

A goal-oriented bot based on a rnn that encodes user utterance and generates response in a sequence-to-sequence manner. For network architecture is similar to [2].

The 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 [1] 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.

For more info on the dataset see KvretDatasetReader.

Here is a simple example of interaction with a trained dialogue bot (available for download):

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.


Config configs/seq2seq_go_bot/bot_kvret_infer.json is recommended to be used for inference (interaction) of a pretrained model.

Config configs/seq2seq_go_bot/bot_kvret.json is recommended to be used for training a new model.


To interact with a pretrained seq2seq_go_bot model using commandline run:

python -m deeppavlov interact <path_to_config> [-d]

where <path_to_config> is one of the provided config files.

You can also train your own model by running:

python -m deeppavlov train <path_to_config> [-d]
The -d parameter downloads:

After downloading required files you can use the configs in your python code.

To infer from a pretrained model with config path equal to <path_to_config>:

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

CONFIG_PATH = '<path_to_config>'
model = build_model_from_config(read_json(CONFIG_PATH))

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