论文标题
在变压器网络上基于梯度的对抗培训，用于检测值得支票的事实主张
Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
论文作者
论文摘要
我们介绍了有关变压器神经网络模型的对抗性训练的功效的研究，以检测值得检查主张的任务。在这项工作中，我们介绍了第一个基于对抗性的，基于变压器的索赔点模型，该模型可在多个具有挑战性的基准上获得最新的结果。我们分别比SoiperBuster数据集和CLEF2019数据集的当前最新模型获得了4.70点F1得分的改进。在此过程中，我们提出了一种将对抗训练应用于变形金刚模型的方法，后者有可能将其推广到许多类似的文本分类任务。除结果外，我们还发布了代码库和手动标记的数据集。我们还通过现场公共API展示了模型的现实世界使用情况。
We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims. In this work, we introduce the first adversarially-regularized, transformer-based claim spotter model that achieves state-of-the-art results on multiple challenging benchmarks. We obtain a 4.70 point F1-score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively. In the process, we propose a method to apply adversarial training to transformer models, which has the potential to be generalized to many similar text classification tasks. Along with our results, we are releasing our codebase and manually labeled datasets. We also showcase our models' real world usage via a live public API.
