Evaluation code for various unsupervised automated metrics for Natural Language Generation.
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README.md

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nlg-eval

Evaluation code for various unsupervised automated metrics for NLG (Natural Language Generation). It takes as input a hypothesis file, and one or more references files and outputs values of metrics. Rows across these files should correspond to the same example.

Metrics

  • BLEU
  • METEOR
  • ROUGE
  • CIDEr
  • SkipThought cosine similarity
  • Embedding Average cosine similarity
  • Vector Extrema cosine similarity
  • Greedy Matching score

Requirements

Tested using

  • Java 1.8.0
  • python 3.6
    • click 6.7
    • nltk 3.3
    • numpy 1.14.5
    • scikit-learn 0.19.1
    • gensim 3.4.0
    • Theano 1.0.2
    • scipy 1.1.0
    • six>=1.11

Python 2.7 has also been tested with mostly the same dependencies but an older version of gensim. You can see the version requirements in requirements_py2.txt

Setup

Install Java 1.8.0 (or higher). Then run:

# Install the Python dependencies.
# It may take a while to run because it's downloading some files. You can instead run `pip install -v -e .` to see more details.
pip install -e .

# Download required data files.
nlg-eval --setup

Usage

Once setup has completed, the metrics can be evaluated with a Python API or in the command line.

Examples of the Python API can be found in test_nlgeval.py.

Standalone

nlg-eval --hypothesis=examples/hyp.txt --references=examples/ref1.txt --references=examples/ref2.txt

where each line in the hypothesis file is a generated sentence and the corresponding lines across the reference files are ground truth reference sentences for the corresponding hypothesis.

functional API: for the entire corpus

from nlgeval import compute_metrics
metrics_dict = compute_metrics(hypothesis='examples/hyp.txt',
                               references=['examples/ref1.txt', 'examples/ref2.txt'])

functional API: for only one sentence

from nlgeval import compute_individual_metrics
metrics_dict = compute_individual_metrics(references, hypothesis)

where references is a list of ground truth reference text strings and hypothesis is the hypothesis text string.

object oriented API for repeated calls in a script - single example

from nlgeval import NLGEval
nlgeval = NLGEval()  # loads the models
metrics_dict = nlgeval.compute_individual_metrics(references, hypothesis)

where references is a list of ground truth reference text strings and hypothesis is the hypothesis text string.

object oriented API for repeated calls in a script - multiple examples

from nlgeval import NLGEval
nlgeval = NLGEval()  # loads the models
metrics_dict = nlgeval.compute_metrics(references, hypothesis)

where references is a list of lists of ground truth reference text strings and hypothesis is a list of hypothesis text strings. Each inner list in references is one set of references for the hypothesis (a list of single reference strings for each sentence in hypothesis in the same order).

Reference

If you use this code as part of any published research, please cite the following paper:

Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. "Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation" arXiv preprint arXiv:1706.09799 (2017)

@article{sharma2017nlgeval,
    author  = {Sharma, Shikhar and El Asri, Layla and Schulz, Hannes and Zumer, Jeremie},
    title   = {Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation},
    journal = {CoRR},
    volume  = {abs/1706.09799},
    year    = {2017},
    url     = {http://arxiv.org/abs/1706.09799}
}

Example

Running

nlg-eval --hypothesis=examples/hyp.txt --references=examples/ref1.txt --references=examples/ref2.txt

gives

Bleu_1: 0.550000
Bleu_2: 0.428174
Bleu_3: 0.284043
Bleu_4: 0.201143
METEOR: 0.295797
ROUGE_L: 0.522104
CIDEr: 1.242192
SkipThoughtsCosineSimilairty: 0.626149
EmbeddingAverageCosineSimilairty: 0.884690
VectorExtremaCosineSimilarity: 0.568696
GreedyMatchingScore: 0.784205

Important Note

CIDEr by default (with idf parameter set to "corpus" mode) computes IDF values using the reference sentences provided. Thus, CIDEr score for a reference dataset with only 1 image (or example for NLG) will be zero. When evaluating using one (or few) images, set idf to "coco-val-df" instead, which uses IDF from the MSCOCO Vaildation Dataset for reliable results. This has not been adapted in this code. For this use-case, apply patches from vrama91/coco-caption.

External data directory

To mount an already prepared data directory to a Docker container or share it between users, you can set the NLGEVAL_DATA environment variable to let nlg-eval know where to find its models and data. E.g.

NLGEVAL_DATA=~/workspace/nlg-eval/nlgeval/data

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

See LICENSE.md.