The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their decision-making in the navigation tasks. To understand the inner workings of deep neural models, various explanation methods have been developed for promoting explainable AI (XAI). But they are mostly applied to deep neural models for image or text classification tasks and little work has been done in explaining deep neural models for VLN tasks. In this paper, we address these problems by building quantitative benchmarks to evaluate explanation methods for VLN models in terms of faithfulness. We propose a new erasure-based evaluation pipeline to measure the step-wise textual explanation in the sequential decision-making setting. We evaluate several explanation methods for two representative VLN models on two popular VLN datasets and reveal valuable findings through our experiments.

翻译：在未知环境中通过自然语言指令导航机器人是实现具身人工智能的关键一步。随着视觉语言导航领域提出的深度神经网络模型性能不断提升，同样值得关注的是模型在导航任务中利用哪些信息进行决策。为理解深度神经网络的内部工作机制，研究者开发了多种解释方法以促进可解释人工智能。但这些方法主要应用于图像或文本分类任务的深度神经网络模型，而针对视觉语言导航任务模型解释的研究十分有限。本文通过构建量化基准，从忠实度角度评估视觉语言导航模型的解释方法。我们提出了一种新的基于擦除的评估流程，用于衡量序列决策场景下的分步文本解释。我们在两个主流视觉语言导航数据集上，对两种代表性视觉语言导航模型的多种解释方法进行了评估，并通过实验揭示了有价值的发现。