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adversarial-paraphrasing-detector

AMHR / adversarial-paraphrasing-detector

Run on huggingface.co
huggingface.co
Total runs: 30
24-hour runs: 0
7-day runs: -8
30-day runs: -199
Model's Last Updated: August 17 2023
text-classification

Introduction of adversarial-paraphrasing-detector

Model Details of adversarial-paraphrasing-detector

This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: https://aclanthology.org/2021.acl-long.552/ . Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git

Please cite the following if you use this model: 

@inproceedings{nighojkar-licato-2021-improving,
    title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
    author = "Nighojkar, Animesh  and
      Licato, John",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.552",
    pages = "7106--7116",
    abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}

Runs of AMHR adversarial-paraphrasing-detector on huggingface.co

30
Total runs
0
24-hour runs
-2
3-day runs
-8
7-day runs
-199
30-day runs

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adversarial-paraphrasing-detector huggingface.co

adversarial-paraphrasing-detector huggingface.co is an AI model on huggingface.co that provides adversarial-paraphrasing-detector's model effect (), which can be used instantly with this AMHR adversarial-paraphrasing-detector model. huggingface.co supports a free trial of the adversarial-paraphrasing-detector model, and also provides paid use of the adversarial-paraphrasing-detector. Support call adversarial-paraphrasing-detector model through api, including Node.js, Python, http.

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https://huggingface.co/AMHR/adversarial-paraphrasing-detector

AMHR adversarial-paraphrasing-detector online free

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https://huggingface.co/AMHR/adversarial-paraphrasing-detector

adversarial-paraphrasing-detector install

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https://huggingface.co/AMHR/adversarial-paraphrasing-detector

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