Here is MeaningBERT

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).

Sanity Check

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT")
model = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT")

or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace

import torch

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT")
scorer = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT")
scorer.eval()

documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."]
simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.",
                   "Whatever, whenever, this is a sentence."]

# We tokenize the text as a pair and return Pytorch Tensors
tokenize_text = tokenizer(documents, simplifications, truncation=True, padding=True, return_tensors="pt")

with torch.no_grad():
    # We process the text
    scores = scorer(**tokenize_text)

print(scores.logits.tolist())

or using our HuggingFace Metric module

import evaluate

documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."]
simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.",
                   "Whatever, whenever, this is a sentence."]

meaning_bert = evaluate.load("davebulaval/meaningbert")

print(meaning_bert.compute(documents=documents, simplifications=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.

davebulaval
/

MeaningBERT