README.md

Neural Baseline Models for DSTC9 Track 1

This directory contains the official baseline codes for DSTC9 Track 1.

It includes the neural baseline models described in the Task Description Paper.

If you want to publish experimental results with this dataset or use the baseline models, please cite the following article:

@article{kim2020domain,
  title={Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access},
  author={Seokhwan Kim and Mihail Eric and Karthik Gopalakrishnan and Behnam Hedayatnia and Yang Liu and Dilek Hakkani-Tur},
  journal={arXiv preprint arXiv:2006.03533}
  year={2020}
}

NOTE: This paper reports the results with an earlier version of the dataset and the baseline models, which will differ from the baseline performances on the official challenge resources.

Getting started

• Clone this repository into your working directory.

$ git clone https://github.com/alexa/alexa-with-dstc9-track1-dataset.git
$ cd alexa-with-dstc9-track1-dataset

• Install the required python packages.

$ pip3 install -r requirements.txt
$ python -m nltk.downloader 'punkt'
$ python -m nltk.downloader 'wordnet

• Train the baseline models.

$ ./train_baseline.sh

• Run the baseline models.

$ ./run_baseline.sh

• Validate the structure and contents of the tracker output.

$ python scripts/check_results.py --dataset val --dataroot data/ --outfile baseline_val.json
Found no errors, output file is valid

• Evaluate the output.

$ python scripts/scores.py --dataset val --dataroot data/ --outfile baseline_val.json --scorefile baseline_val.score.json

• Print out the scores.

$ cat baseline_val.score.json | jq
{
  "detection": {
    "prec": 0.9526584122359796,
    "rec": 0.978675645342312,
    "f1": 0.9654917881527957
  },
  "selection": {
    "mrr@5": 0.7602017592421715,
    "r@1": 0.6613766377560436,
    "r@5": 0.8894630005536076
  },
  "generation": {
    "bleu-1": 0.3494779829369024,
    "bleu-2": 0.21592516786059082,
    "bleu-3": 0.13566462884792285,
    "bleu-4": 0.092676787701347,
    "meteor": 0.35154549916793787,
    "rouge_1": 0.38418363055958615,
    "rouge_2": 0.17156023928989267,
    "rouge_l": 0.34207981732502507
  }
}