MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was
proposed in our
article MeaningBERT: assessing meaning preservation between sentences.
Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity
checks. For more details, refer to our publicly available article.
This public version of our model uses the best model trained (where in our article, we present the performance results
of an average of 10 models) for a more extended period (500 epochs instead of 250). We have observed later that the
model can further reduce dev loss and increase performance. Also, we have changed the data augmentation technique used
in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a).
Correlation to human judgment is one way to evaluate the quality of a meaning preservation metric.
However, it is inherently subjective, since it uses human judgment as a gold standard, and expensive since it requires
a large dataset
annotated by several humans. As an alternative, we designed two automated tests: evaluating meaning preservation between
identical sentences (which should be 100% preserving) and between unrelated sentences (which should be 0% preserving).
In these tests, the meaning preservation target value is not subjective and does not require human annotation to
be measured. They represent a trivial and minimal threshold a good automatic meaning preservation metric should be able to
achieve. Namely, a metric should be minimally able to return a perfect score (i.e., 100%) if two identical sentences are
compared and return a null score (i.e., 0%) if two sentences are completely unrelated.
Identical Sentences
The first test evaluates meaning preservation between identical sentences. To analyze the metrics' capabilities to pass
this test, we count the number of times a metric rating was greater or equal to a threshold value X∈[95, 99] and divide
It is calculated by the number of sentences to create a ratio of the number of times the metric gives the expected rating. To account
for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a threshold value of
100%.
Unrelated Sentences
Our second test evaluates meaning preservation between a source sentence and an unrelated sentence generated by a large
language model.3 The idea is to verify that the metric finds a meaning preservation rating of 0 when given a completely
irrelevant sentence mainly composed of irrelevant words (also known as word soup). Since this test's expected rating is
0, we check that the metric rating is lower or equal to a threshold value X∈[5, 1].
Again, to account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use
a threshold value of 0%.
Use MeaningBERT
You can use MeaningBERT as a model that you can retrain or use for
inference using the following with HuggingFace
or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace
python
1import torch
23from transformers import AutoTokenizer, AutoModelForSequenceClassification
45tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT")6scorer = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT")7scorer.eval()89documents =["He wanted to make them pay.","This sandwich looks delicious.","He wants to eat."]10simplifications =["He wanted to make them pay.","This sandwich looks delicious.",11"Whatever, whenever, this is a sentence."]1213# We tokenize the text as a pair and return Pytorch Tensors14tokenize_text = tokenizer(documents, simplifications, truncation=True, padding=True, return_tensors="pt")1516with torch.no_grad():17# We process the text18 scores = scorer(**tokenize_text)1920print(scores.logits.tolist())
or using our HuggingFace Metric module
python
1import evaluate
23documents =["He wanted to make them pay.","This sandwich looks delicious.","He wants to eat."]4simplifications =["He wanted to make them pay.","This sandwich looks delicious.",5"Whatever, whenever, this is a sentence."]67meaning_bert = evaluate.load("davebulaval/meaningbert")89print(meaning_bert.compute(references=documents, predictions=simplifications))
Cite
Use the following citation to cite MeaningBERT
@ARTICLE{10.3389/frai.2023.1223924,
AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard},
TITLE={MeaningBERT: assessing meaning preservation between sentences},
JOURNAL={Frontiers in Artificial Intelligence},
VOLUME={6},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924},
DOI={10.3389/frai.2023.1223924},
ISSN={2624-8212},
}
Contributing to MeaningBERT
We welcome user input, whether it regards bugs found in the library or feature propositions! Make sure to have a
look at our contributing guidelines
for more details on this matter.
License
MeaningBERT is MIT licensed, as found in
the LICENSE file.
