EvalEval

This repository contains the code for the paper Perturbation CheckLists for Evaluating NLG Evaluation Metrics to appear at EMNLP, 2021.

Authors: Ananya B. Sai, Tanay Dixit, Dev Yashpal Sheth, Sreyas Mohan and Mitesh M. Khapra.

Webpage: https://iitmnlp.github.io/EvalEval/

Contents

• Overview
• Setup
• Templates
  • Data-to-Text Generation
  • Image Captioning
  • Translation
  • Dialogue
  • Summarization
  • Question Generation
• Human Evaluations
• Metrics
• Citation

Overview

In this work we provide a detailed analysis of NLG metrics by going beyond correlation with human scores. We propose a comprehensive criteria-checklist based evaluation that will act as a diagnostic tool in pointing out specific avenues of improvement in metrics. We create specific templates that are targeted to evaluate the ability of a metric to capture a particular dimension.

Please find more details of this work in our paper.

Setup

Install Dependencies

Our code is based on python 3.7 and to install all the dependencies run the following command.

pip install -r requirements.txt

Load the data

All the original datasets used in our experiments can be directly downloaded by running the following command.

cd data
bash download.sh

To use custom datasets please follow the following format or feel free to make changes in the code to make it compatible.
jsonl format

{'id': 0, 'references':'Tom went to play in the garden', ...}
{'id': 1, 'references':'It will rain today', ...}
.
.

csv format

id, references, ...
0 , Tom went to play in the garden, ..
1 , It will rain today, ..

Note: DG follows a different format than the rest

Templates

All the templates used in our works have been made available in the templates/ folder and are categorized in the following sections.

All tasks have the following criteria, the table can also be found in our paper.

All the templates save the perturbed sentences along with the original in the outputs folder. To test the metrics performance on these, pass the reference and perturbed sentences and compare the aggregated metric score over the entire dataset with the annotations score given for every template. More details can be found in the metrics section.

Data-to-Text Generation

To run the perturbations use the following command.

python3 main.py \
        --task D2T  \
        --ref_file data/<data.jsonl> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Coverage/Relevance>

Image Captioning

To run the perturbations use the following command.

python3 main.py \
        --task IC  \
        --ref_file data/<data.jsonl> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Completeness/Throughness>

Machine Translation

To run the perturbations use the following command.

python3 main.py \
        --task MT  \
        --ref_file data/<data.jsonl> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Adequacy>

Dialogue Generation

To run the perturbations use the following command.

python3 main.py \
        --task DG  \
        --ref_file data/<data.csv> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Avoid-repetition/Making-sense>

Abstrative Summarization

To run the perturbations use the following command.

python3 main.py \
        --task AS  \
        --ref_file data/<data.jsonl> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Coverage/Relevance/Clarity>

Question Generation

To run the perturbations use the following command.

python3 main.py \
        --task QG  \
        --ref_file data/<data.jsonl> \
        --output_file example \
        --criteria <all/Fluency/Invariance/Answerability>

Human Evaluations

The human annotations collected for the perturbation templates can be downloaded from here.

We also used the human judgement scores collected along multiple criteria for different tasks from the following sources:

Metrics

We followed the implementation of metrics with the help of the following repositories: For BLEU, METEOR, ROUGE-L, CIDEr, Embedding Averaging, Greedy Matching, and Vector Extrema, we use the implementation provided by Sharma et al. (2017). For chrF++, TER, BERTScore, and BLEURT, we use the repository of Castro Ferreira et al. (2020). For SMS, WMDo, and Mover-Score, we use the implementation provided by Fabbri et al. (2020). For all the remaining task-specific metrics, we use the official codes from the respective papers.

Citation

@InProceedings{Sai_2021_EMNLP,
    author = {Sai, Ananya B. and Dixit, Tanay and Sheth, Dev Yashpal and Mohan, Sreyas and Khapra, Mitesh M.},
    title = {Perturbation CheckLists for Evaluating NLG Evaluation Metrics},
    booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    month = {November},
    year = {2021}
}