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Hierarchical intent and slot filling

In this tutorial, we will train a semantic parser for task oriented dialog by modeling hierarchical intents and slots (Gupta et al. , Semantic Parsing for Task Oriented Dialog using Hierarchical Representations, EMNLP 2018). The underlying model used in the paper is the Recurrent Neural Network Grammar (Dyer et al., Recurrent Neural Network Grammar, NAACL 2016). RNNG is neural constituency parser that explicitly models the compositional tree structure of the words and phrases in an utterance.

1. Fetch the dataset

Download the dataset to a local directory. We will refer to this as base_dir in the next section.

$ curl -o top-dataset-semantic-parsing.zip -L https://fb.me/semanticparsingdialog
$ unzip top-dataset-semantic-parsing.zip

2. Prepare configuration file

Prepare the configuration file for training. A sample config file can be found in your PyText repository at demo/configs/rnng.json. If you haven't set up PyText, please follow :doc:`installation`, then make the following changes in the config:

  • Set train_path to base_dir/top-dataset-semantic-parsing/train.tsv.
  • Set eval_path to base_dir/top-dataset-semantic-parsing/eval.tsv.
  • Set test_path to base_dir/top-dataset-semantic-parsing/test.tsv.

3. Train a model with the downloaded dataset

Train the model using the command below

(pytext) $ pytext train < demo/configs/rnng.json

The output will look like:

Merged Intent and Slot Metrics
P = 24.03 R = 31.90, F1 = 27.41.

This will take about hour. If you want to train with a smaller dataset to make it quick then generate a subset of the dataset using the commands below and update the paths in demo/configs/rnng.json:

$ head -n 1000 base_dir/top-dataset-semantic-parsing/train.tsv > base_dir/top-dataset-semantic-parsing/train_small.tsv
$ head -n 100 base_dir/top-dataset-semantic-parsing/eval.tsv > base_dir/top-dataset-semantic-parsing/eval_small.tsv
$ head -n 100 base_dir/top-dataset-semantic-parsing/test.tsv > base_dir/top-dataset-semantic-parsing/test_small.tsv

If you now train the model with smaller datasets, the output will look like:

Merged Intent and Slot Metrics
P = 24.03 R = 31.90, F1 = 27.41.

4. Test the model interactively against input utterances.

Load the model using the command below

(pytext) $ pytext predict-py --model-file=/tmp/model.pt
please input a json example, the names should be the same with column_to_read in model training config:

This will give you a REPL prompt. You can enter an utterance to get back the model's prediction repeatedly. You should enter in a json format shown below. Once done press Ctrl+D.

{"text": "order coffee from starbucks"}

You should see an output like:

[{'prediction': [7, 0, 5, 0, 1, 0, 3, 0, 1, 1],
'score': [
        0.44425372408062447,
        0.8018286800064633,
        0.6880680051949267,
        0.9891564979506277,
        0.9999506231665385,
        0.9992705616574005,
        0.34512090135492923,
        0.9999979545618913,
        0.9999998668826438,
        0.9999998686418744]}]

We have also provided a pre-trained model which you may download here