Awesome

Mapping natural language commands to web elements

Data

Due to its large size, the data is hosted outside Github: https://nlp.stanford.edu/projects/phrasenode/

You can download the dataset by running the script as follows.

bash download_dataset.sh

Setup

The code was developed in the following environment:

• Python 2.7
• Pytorch 0.4.1
• CUDA 9

To install dependencies:

• (Optional) Create a virtualenv / conda environment

  virtualenv.py -p python2.7 env
  source env/bin/activate
  

• Python dependencies

  sudo apt-get install python-dev
  pip install -r requirements.txt
  

Alternatively, use the docker image ppasupat/phrasenode For latest image: docker pull ppasupat/phrasenode:1.06

Quick start

If you just want to see something happen:

export WEBREP_DATA=./data
./main.py configs/base.txt configs/model/encoding.txt configs/node-embedder/allan.txt -n testrun

• This executes the main entrypoint script main.py with three config files.
• The config files are specified in HOCON format. If multiple files are specified, they will be merged together (with later configs overwriting values from previous ones).
• You can also add ad-hoc config strings using the -c option. These are applied last.
• -n specifies the experiment directory name.

Configurations

Here are the configerations used in the final experiments:

• base.txt: Used in all experiments
• models/encoding.txt: The embedding-based method
• models/alignment.txt: The alignment-based method
• node-embedder/allan.txt: The node embedder as described in the paper
• ablation/*.txt: Ablation

Note that the visual neighbor is off by default. To turn it on, use general/neighbors.txt.

Experiment management

All training runs are managed by the PhraseNodeTrainingRuns object. For example, to get training run #141, do this:

runs = PhraseNodeTrainingRuns()   # Note the final "s"
run = runs[141]  # a PhraseNodeTrainingRun object

A TrainingRun is responsible for constructing a model, training it, saving it and reloading it (see superclasses gtd.ml.TrainingRun and gtd.ml.TorchTrainingRun for details.)

The most important methods on PhraseNodeTrainingRun are:

• __init__: the model, data storage, etc, are initialized
• train: actual training of the model happens here

TensorBoard

Statistics are logged to TensorBoard. To view:

tensorboard --logdir=data/experiments

Demo Chrome extension

• Start the server with

  export WEBREP_DATA=./data
  ./server.py data/experiments/0_testrun/config.txt -m data/experiments/0_testrun/checkpoints/20000.checkpoint/model
  

  where 0_testrun should be changed to the model's directory, and 20000 should be changed to the checkpoint number you want.

• Install the unpacked Chrome extension in demo/phrasenode-demo

  • Follow the instruction from here to load the extension. (The manifest file should already be in demo/phrasenode-demo)
  • An extension button should now show up on the toolbar
  • On any web page, click on the extension button and enter a phrase in the prompt that pops up.
  • If the server does not throw an error, the selected element should be highlighted with a red border. Details can be viewed in the developer console.

Referenece

Panupong Pasupat, Tian-Shun Jiang, Evan Liu, Kelvin Guu, Percy Liang.
Mapping natural language commands to web elements.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.

CodaLab: https://worksheets.codalab.org/worksheets/0x0097f249cd944284a81af331093c3579/