T5 For Adversarial Paraphrasing

🚀 パラフレーズモデル

このモデルは、論文「https://aclanthology.org/2021.acl-long.552/」で説明され使用されている敵対的パラフレーズタスク向けに設計されたパラフレーザーです。

GitHubリポジトリのnap_generation.pyを参照することで、top-kサンプリングとtop-pサンプリングの概念を用いてこのモデルをより良く活用する方法を学ぶことができます。Hugging Faceのデモでは、モデルがビームサーチとサンプリングを使用して出力するため、入力文とほぼ同じ1文のみが出力されます。

📦 リンク情報

• GitHubリポジトリ: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git

📄 引用情報

このモデルを使用する場合は、以下を引用してください。

@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.",
}