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 .
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  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
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]
<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]
After downloading required files you can use the configs in your python code.
To infer from a pretrained model with config path equal to
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])) utterance = input(':: ')