论文标题
为什么强大的自然语言理解是一个挑战
Why Robust Natural Language Understanding is a Challenge
论文作者
论文摘要
随着深度机器学习到现实生活应用中的扩散，该技术的一种特殊属性引起了人们的注意：鲁棒性神经网络众所周知，鲁NTORIDES呈现较低的鲁棒性，并且对小输入扰动非常敏感。最近，已经提出了许多用于验证网络鲁棒性一般特性的方法，但它们主要用于计算机视觉。在本文中，我们提出了基于较大感兴趣区域的自然语言理解分类的验证规范，我们讨论了此类任务的挑战。我们观察到，尽管数据几乎是线性可分离的，但验证者努力输出积极的结果，我们解释了问题和含义。
With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks notoriously present low robustness and can be highly sensitive to small input perturbations. Recently, many methods for verifying networks' general properties of robustness have been proposed, but they are mostly applied in Computer Vision. In this paper we propose a Verification specification for Natural Language Understanding classification based on larger regions of interest, and we discuss the challenges of such task. We observe that, although the data is almost linearly separable, the verifier struggles to output positive results and we explain the problems and implications.
