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Fix calculation error when ref is empty #92

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@voidful voidful commented Jan 5, 2020

This pull request fixing the issue that any of the ref is empty

When the number of ref is inconsistent, i will fill a empty string as padding. which causing an error.

scores = n.compute_metrics(ref_list=[
            [
                "this is one reference sentence for sentence1",
                ""
            ],
            [
                "this is one more reference sentence for sentence1",
                "this is the second reference sentence for sentence2"
            ],
        ],
            hyp_list=[
                "this is the model generated sentence1 which seems good enough",
                "this is sentence2 which has been generated by your model"
            ]
        )

@msftclas
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msftclas commented Jan 5, 2020

CLA assistant check
All CLA requirements met.

@juharris
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juharris commented Jan 5, 2020

Thanks for pointing this out. The references are the targets that the generated hypothesis should match. It's possible that a target would indeed be an empty string so I think we should correct what is actually causing the error instead of silently ignoring empty strings which could mean that a hypothesis is compared with a target that it wasn't meant to be compared with. For example:

References:

  1. "Sentence 1"
  2. "" (this one would get filtered out)
  3. "Sentence 3"

Hypotheses:

  1. "Sentence 1"
  2. "Sentence 2"
  3. "Sentence 3"

@voidful
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voidful commented Jan 6, 2020

I agree that we should correct what is actually causing the error
To clear this in more general way:
when one of the ref is empty or hyp is empty

ref=["this is a test",""],
hyp="this is a good test"
ref=["this is a good test"],
hyp=""

vectorize metric(Skip-thought/ glove_metrics) will cause error due to empty input to encode
so the following commit will try to correct it.

.gitignore Outdated
@@ -22,3 +22,30 @@ glove2word2vec.py

.idea/
venv/

# General
.DS_Store
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Can you remove changes to your gitignore? They seem rather specific to your setup.

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Ok I can remove it.

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@juharris Thank you for your advice. It is what I need.


scores = n.compute_individual_metrics(ref=["Chocolate and an Interview "],
hyp="Chocolate , a Healthy Food")
print(scores)
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Can you assert something here, so that tests can detect regressions?

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I found that this case is not necessary. I will remove it

hypothesis = os.path.join(root_dir, 'examples/6.csv')
references = os.path.join(root_dir, 'examples/7.pt_beam_1.csv')
scores = nlgeval.compute_metrics(hypothesis, [references])
print(scores)
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Same, better to assert the result you expect.

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I found that this case is not necessary. I will remove it

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4 participants