A neural-network based semantic parser for the Almond virtual assistant.

Almond is a research project of the Mobile and Social Computing Lab at Stanford University. You can find more information at http://almond.stanford.edu/about and https://mobisocial.stanford.edu.

Almond NN-Parser depends on numpy, Tensorflow 1.2 for Python 3, and the OrderedSet module from PIP:

sudo dnf install python3-numpy
pip3 install tensorflow orderedset

It's recommended to install numpy from distribution packages, not pip because it's faster and more reliable. The rest is not available at least in Fedora. Optionally, you'll want to install python3-matplotlib-gtk3 for the visualizations.

Please see the Tensorflow documentation if you wish to use GPU acceleration (you'll need to install nvidia+cuda or amdgpu-pro+rocm drivers).

Almond NN-Parser has been tested successfully on Fedora 25 and 26 x86_64 with CPU and Nvidia GPU acceleration.

Training assumes that you acquired the dataset in preprocessed form from somewhere (e.g. as part of the supplementary material for a paper that uses Almond NN-Parser).

1. Set the DATASET environment variable to the directory containing the dataset *.tsv files
2. Download the word embeddings. We recommend using Glove 42B 300-dimensional, which can be downloaded from http://nlp.stanford.edu/data/glove.42B.300d.zip. Set the GLOVE environment variable to the path of uncompressed text file.
3. Prepare the working directory:

    mkdir ~/workdir
    cd ~/workdir
    ~/almond-nnparser/scripts/prepare.sh . ${SNAPSHOT}

This script computes the input dictionary, downloads a snapshot of Thingpedia, and computes a subset of the word embedding matrix to make it faster to load. Use the snapshot argument to choose which Thingpedia snapshot to train against, or pass -1 for the latest content of Thingpedia. Be aware that using -1 might make the results impossible to reproduce. 4. Check that the dataset is compatible with the Thingpedia snapshot:

    cut -f2 ${DATASET}/*.tsv > programs.txt
    cd ~/almond-nnparser
    python3 -m grammar.thingtalk ~/workdir/thingpedia.txt < ~/workdir/programs.txt

5. Prepare a model directory, eg. model.1, inside the working directory, and create a model.conf inside it. Edit any model parameters that you wish.
6. Train:

    ~/almond-nnparser/run_train.py ./model.1 ${DATASET}/train.tsv ${DATASET}/dev.tsv

7. Visualize the training:

    ~/almond-nnparser/scripts/plot_learning.py ./model.1/train-stats.json

8. Test:

    ~/almond-nnparser/run_test.py ./model.1 ${DATASET}/test.tsv

After training, a server which is compatible with Almond can be run from the trained working directory:

~/almond-nnparser/run_server.py en:./model.1

By default, the server runs at port 8400. You can change that with a server.conf file. You can change the paths in model.conf if you wish to run the server in a different directory.

The server can also be run for multiple languages, as in:

~/almond-nnparser/run_server.py en:./model.1 es:./model.2 zh:./model.3

The server expects to connect to a TokenizerService (provided by SEMPRE) on localhost, port 8888.