T5 For Adversarial Paraphrasing

🚀 释义生成模型

本模型是一个释义生成器，专为论文 Improving Paraphrase Detection with the Adversarial Paraphrasing Task 中描述和使用的对抗性释义任务而设计。该模型能够生成语义等效但词汇和句法不同的释义。

🚀 快速开始

若要更好地利用此模型，可参考 GitHub 仓库中的 nap_generation.py 文件，其中涉及了 top-k 采样和 top-p 采样的概念。需要注意的是，Hugging Face 上的演示仅会输出一个句子，且很可能与输入句子相同，因为该模型原本是使用束搜索和采样进行输出的。

GitHub 仓库地址：https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git

📄 许可证

如果您使用了此模型，请引用以下文献：

@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.",
}