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DA-Time Temporal Tagger

a domain-adapted temporal expression recognizer for the English Voice Assistant domain

Citing DA-Time

To refer to DA-Time in any publication, please cite the following paper:

Alam, T., Zarcone, A., & Padó, S. (2021). New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger To the Voice Assistant Domain. In Proceedings of the 14th International Conference on Computational Semantics (IWCS 2021).

@inproceedings{alam2021:new,
    author       = {Touhidul Alam and Alessandra Zarcone and Sebastian Pad\'{o}},
    title        = {{New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger To the Voice Assistant Domain}},
    year         = 2021,
    booktitle    = {{Proceedings of the 14th International Conference on Computational Semantics (IWCS 2021)}}
    }

Getting Started

Installation Guide

  • Clone the repository 
    git clone https://github.com/audiolabs/DA-Time.git
  • Install Python >= 3.7.3
  • Make sure to have virtualenv installed by executing 
    python -m pip install --user virtualenv
  • Create the virtual environment 
    python -m venv <path-to-env>
  • Source the environment 
    source <path-to-env>/bin/activate
  • Install the following packages 
    pip install -r requirements.txt
  • Download Spacy English model 
    python -m spacy download en_core_web_sm
  • Download Stanford CoreNLP 4.0.0 (English model) 
    https://stanfordnlp.github.io/CoreNLP/download.html

Running Guide

  • Make sure to check the necessary experimental setup from the config.yaml file.

  • To run the model with different mode (Training/testing/fine-tuning) run the following command

    python main.py exec --mode=<train/test/ft>

  • [Optional] Parameter for multiple run (these will overwrite configs value)

    python main.py exec --mode=<train/test/ft> [--run=<run>] [--amount=<amount>] [--emb=<emb>]

    Here, run indicates #of run, amount is to define fine-tune data amount%, emb is the model embedding. To run multiple times, edit and use the script:

    ./RUN.sh

  • To plot different data statistics run the following command

    python main.py plot

  • To predict on a trained model, run the following command with n-number of sentences as argument

    python main.py predict "when is our tomorrow's meeting?" "Make a schedule from Friday to Monday"

  • To simplify text with the CoreNLP run the following command in terminal

    java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000000

  • In parallel, run the simplifier script with this commands:

    cd recognition
python simplifier.py