论文标题
插入和播放反事实的文本生成，以符合模型的鲁棒性
Plug and Play Counterfactual Text Generation for Model Robustness
论文作者
论文摘要
生成反事实测试箱是测试NLP模型并使其像传统软件一样坚固且可靠的重要主体。在生成测试箱时，所需的特性是能够以灵活的方式控制测试案例生成以测试各种故障案例，并以目标方式解释和修复它们。在这个方向上，通过手动编写生成受控反事实的规则，在先前的作品中取得了重大进展。但是，这种方法需要大量的手动监督，并且缺乏轻松引入新控件的灵活性。由PPLM的插件方法令人印象深刻的灵活性的激励，我们建议将插件的框架带入反事实测试案例生成任务。我们介绍了Casper，这是一种插件的反事实生成框架，以生成满足按需目标属性的测试用例。我们的插件模型可以在给定任何属性模型的情况下引导测试案例生成过程，而无需对模型的特定属性培训。在实验中，我们表明Casper有效地生成了反事实文本，该文本遵循属性模型提供的转向，同时流利，多样化并保留原始内容。我们还表明，Casper的生成的反事实可用于增强训练数据，从而固定和使测试模型更强大。
Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case generation in a flexible manner to test for a large variety of failure cases and to explain and repair them in a targeted manner. In this direction, significant progress has been made in the prior works by manually writing rules for generating controlled counterfactuals. However, this approach requires heavy manual supervision and lacks the flexibility to easily introduce new controls. Motivated by the impressive flexibility of the plug-and-play approach of PPLM, we propose bringing the framework of plug-and-play to counterfactual test case generation task. We introduce CASPer, a plug-and-play counterfactual generation framework to generate test cases that satisfy goal attributes on demand. Our plug-and-play model can steer the test case generation process given any attribute model without requiring attribute-specific training of the model. In experiments, we show that CASPer effectively generates counterfactual text that follow the steering provided by an attribute model while also being fluent, diverse and preserving the original content. We also show that the generated counterfactuals from CASPer can be used for augmenting the training data and thereby fixing and making the test model more robust.
