metadata
language:
- en
tags:
- paraphrase detection
licenses:
- cc-by-nc-sa
Model overview
Mutual Implication Score: a symmetric measure of text semantic similarity based on a RoBERTA model pretrained for natural language inference and fine-tuned for paraphrase detection. It is particularly useful for paraphrases detection
How to use
The following snippet illustrates code usage:
!pip install mutual-implication-score
from mutual_implication_score import MIS
mis = MIS(device='cpu')
source_texts = ['I want to leave this room',
'Hello world, my name is Nick']
paraphrases = ['I want to go out of this room',
'Hello world, my surname is Petrov']
scores = mis.compute(source_texts, paraphrases)
print(scores)
# expected output: [0.9748, 0.0545]
Model details
We slightly modify RoBERTa-Large NLI model architectures (see the scheme below) and fin-tune it with QQP paraphrases dataset.
Performance on Text Style Transfer and Paraphrase Detection tasks
This measure was developed in terms of large scale comparison of different measures on text style transfer and paraphrases datasets.
Citations
If you find this repository helpful, feel free to cite our publication:
@inproceedings{babakov-etal-2022-large,
title = "A large-scale computational study of content preservation measures for text style transfer and paraphrase generation",
author = "Babakov, Nikolay and
Dale, David and
Logacheva, Varvara and
Panchenko, Alexander",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.23",
pages = "300--321",
abstract = "Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced. In both tasks, the system is supposed to generate a text which should be semantically similar to the input text. Therefore, these tasks are dependent on methods of measuring textual semantic similarity. However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text. According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neural-based that significantly outperform classic approaches. The current problem is the lack of a thorough evaluation of the available measures. We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets. We show that measures based on cross-encoder models outperform alternative approaches in almost all cases.We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.",
}
Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
