Sequence-To-Sequence Dialogue Bot For Goal-Oriented Task¶
Pretrained on KVRET dataset (English) model is available. Try it by running:
from deeppavlov import build_model, configs
bot = build_model(configs.seq2seq_go_bot.bot_kvret, download=True)
dialog_id = '2b77c100-0fec-426a-a483-04ac03763776'
bot(['Hi! Where is the nearest gas station?'], [dialog_id])
bot(['Thanks, bye'], [dialog_id])
If you’re missing some required packages, install all the requirements by running in command line:
python -m deeppavlov install bot_kvret
Intro¶
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 [1].
Diagram 1. Key-value retrieval network.
For each time-step of decoding, the cell state is used to compute
an attention over the encoder states and a separate attention
over the key of each entry in the KB. The attentions over the encoder
are used to generate a context vector which is combined with the cell state to
get a distribution over the normal vocabulary.
The attentions over the keys of the KB become the logits
for their associated values and are separate entries in a
now augmented vocabulary that we argmax over.
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 [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.
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 is the nearest gas station?
dialog_id:: 2b77c100-0fec-426a-a483-04ac03763776
>> chevron is the nearest gas station at 783 arcadia pl.
x_text:: How far is chevron?
dialog_id:: 2b77c100-0fec-426a-a483-04ac03763776
>> chevron is the closest at 783 arcadia pl.
x_text:: Ok, is there traffic?
dialog_id:: 2b77c100-0fec-426a-a483-04ac03763776
>> there is heavy traffic on the route to chevron.
x_text:: Thanks
dialog_id:: 2b77c100-0fec-426a-a483-04ac03763776
>> you're welcome. end_of_dialogue
x_text:: Will it be rainy on tuesday in Camarillo?
dialog_id:: 3a3f05ec-a5db-4ba5-add8-4ffe02758c35
>> it will not be cloudy in camarillo on tuesday.
x_text:: Thank you car!
dialog_id:: 3a3f05ec-a5db-4ba5-add8-4ffe02758c35
>> you're welcome! end_of_dialogue
x_text:: When is my tennis activity?
dialog_id:: 0b144470-faa8-4e71-98c7-a9b645fffab0
>> your tennis activity is on sunday at 4 pm.
x_text:: With whom is it?
dialog_id:: 0b144470-faa8-4e71-98c7-a9b645fffab0
>> your tennis activity on sunday at 4 pm is with your father.
x_text:: Ok, good day to you
dialog_id:: 0b144470-faa8-4e71-98c7-a9b645fffab0
>> you are welcome. end_of_dialogue
x_text:: exit
For every dialog_id that was preset in train, dev or test dataset there exists a list of entries in knowledge base. The entries are being used by model for response prediction. To see available dialogs ids and corresponding knowledge base items see kvret_dataset files.
In the dialogs above, dialog with dialog_id="2b77c100-0fec-426a-a483-04ac03763776" had entry:
{
"distance": "5 miles",
"traffic_info": "heavy traffic",
"poi_type": "gas station",
"address": "783 Arcadia Pl",
"poi": "Chevron"
}
Dialog with dialog_id="3a3f05ec-a5db-4ba5-add8-4ffe02758c35" had entry:
{
"monday": "clear skies, low of 90F, high of 100F",
"tuesday": "cloudy, low of 90F, high of 100F",
"friday": "overcast, low of 90F, high of 100F",
"wednesday": "windy, low of 30F, high of 50F",
"thursday": "snow, low of 90F, high of 100F",
"sunday": "rain, low of 60F, high of 70F",
"location": "camarillo",
"saturday": "overcast, low of 60F, high of 80F",
"today": "monday"
}
Dialog with dialog_id="0b144470-faa8-4e71-98c7-a9b645fffab0" had entry:
{
"room": "-",
"agenda": "-",
"time": "4pm",
"date": "sunday",
"party": "father",
"event": "tennis activity"
}
Note
The model uses massive fasttext embeddings and thus requires 10Gb of disk space.
Configs¶
Config configs/seq2seq_go_bot/bot_kvret.json is recommended to be used for inference (interaction) of a pretrained model.
Config configs/seq2seq_go_bot/bot_kvret_train.json is recommended to be used for training a new model.
Usage¶
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
-dparameter downloads: - data required to train your model (embeddings, etc.);
- a pretrained model when you use configs/seq2seq_go_bot/bot_kvret.json or configs/seq2seq_go_bot/bot_kvret_train.json.
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 import build_model
CONFIG_PATH = '<path_to_config>'
model = build_model(CONFIG_PATH)
utterance = ""
dialog_id = '2b77c100-0fec-426a-a483-04ac03763776' # or any other dialog id from train dataset
while utterance != 'exit':
print(">> " + model([utterance], [dialog_id])[0])
utterance = input(':: ')
Config parameters:¶
To configure your own pipelines that contain a "seq2seq_go_bot" component, refer to documentation for Seq2SeqGoalOrientedBot and Seq2SeqGoalOrientedBotNetwork classes.