arxiv.org/abs/2004.02990

Our code and data are a platform for evaluating NLG diversity metrics.

• Data used for our experiments
• McDiv dataset

• Running the metrics used in the paper (and easily add your own)
• Running all of our experiments (and easily add your own)

If you are also running the code, the data will be downloaded automatically. Otherwise, the data can be downloaded manually from here.

The code is running on python3.

• For running neural metrics, both tensorflow >= 1.12 and pytorch >= 1.0.1 are needed.
• For running BERT-score, install pip install bert_score
• For running sent-BERT, install pip install sentence_transformers
• Running BERT-sts is less straightforward; you can either mute it by turning BertSts's use_me = False in diversity_metrics.py or do the followings:
  • clone github.com/GuyTevet/bert-sts.git to ../bert-sts
  • unzip checkpoints to ../bert-sts/sts_output

For running all metrics over all the data, use:

python run_metrics.py

This will download the data with all the metrics already calculated, and do nothing because they are already calculated ;) If you wish to specify file or directory and override it with local metrics calculations, you can run for example:

python run_metrics.py --input_csv ./data/raw/McDiv_nuggets --override

If you wish to specify metrics to run, you can also add them by their class name (comma separated) like this:

python run_metrics.py --metrics BertSts,AveragedCosineSimilarity

Your new metric must be impemented in diversity_metrics.py and include the static variables:

use_me = True
default_config = {} # your config comes here

If you wish to implement a plain diversity metric (that is not derived from a similarity metric), just inherit metric.DiversityMetric and implement the __init__ and __call__ methods. You can take DistinctNgrams as a code example.

If you wish to implement a diversity metric that derived from a similarity metric, first implement your similarity metric in similarity_metrics.py inherit metric.SimilarityMetric, take for example CosineSimilarity. Then, in diversity_metrics.py, implement the derived diversity metric, inherit metric.Similarity2DiversityMetric, and specify your similarity metric at the __call__ method, like in CosineSimilarity2Diversity.

Note that for neural metrics, we use the more complex metric.Similarity2DiversityFromFileMetric base class, which also includes caching.

For running all experiments over all the data, use:

python run_experiments.py

This script will automatically download the data if it's not already exists.

If you prefer to specify yourself the experiments to run, you can run for example:

python run_experiments.py --input_json ./data/experiments/dec_test_200.json,./data/experiments/mcdiv_nuggets.json

If you want to define a new experiment that uses one of the existing tests (decTest or conTest) but with different data or metrics, you can add a .json file that defines the experiment in ./data/experiments/. The .json file should be writen using the following template:

{
  "global_config": {
    "class_name": "DecTest" # or "ConTest"
  },
  "experiments": {
    "dataset_a": "data/with_metrics/path_to_dataset_a.csv",
    "dataset_b": "data/with_metrics/path_to_dataset_b.csv",
    ...
  }
}

You can take ./data/experiments/dec_test_200.json as a reference.

To implement a new test (that checks the correlation of metrics with a different diversity parameter):

• Implement your test as a class that inherits metrics_test.MetricsTest and override __init__, check_config, collect_data, run, visualize and export methods. Take dec_test.DecTest as a reference.
• In run_experiments.py, import your test and add it to test_classes.
• Add an experiment that runs your test as explained in the previous section.