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Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation

Code and Data for our Findings of ACL 2021 paper titled 'Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation. Varun Gangal *, Harsh Jhamtani *, Eduard Hovy, Taylor Berg-Kirkpatrick'

Data

  • Relevant original and augmented reference files in are present in 'ref_files/' in the required format
  • Human ratings file: 'human_rating_correlation/mturk_rating_processed_output.csv'. Please consider citing Gupta et al if you use the human ratings file.

Code

Code and script to compute metric correlations with human ratings can be found in 'human_rating_correlation/' directory

Requirements

  • Python 3.7.5
  • bert_score (0.3.7)
  • nlgeval(Accessed: December 2020)
  • scipy 1.1.0

Citation

@inproceedings{acl2021dialogeval, 
title={Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation}, 
author={Gangal, Varun and Jhamtani, Harsh and Hovy, Ed and Berg-Kirkpatrick, Taylor}, 
booktitle={Findings of ACL}, 
year={2021} 
}

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diverse-reference-augmentation's Issues

--max_num_multi_response to reproduce the paper results

Hi! First of all, thank you for sharing your work.

I hope to reproduce the results of SCARCE in a multi-reference setup. In this case, how should I set the value of --max_num_multi_response parameter? Is it okay to set the value as -1 (default)?

Thank you!

question about dialog retrieval

So thanks for sharing your work !

I have a few questions about the dialog retrieval task.

  1. For each utterance,how many references to recall as the pseudo reference? 2) the dialog corpus you use to retrieval

Hope to your reply

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