论文标题
在NLP分类中考虑用遮挡和语言建模的可能性解释
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
论文作者
论文摘要
最近，最新的NLP模型获得了对语言的句法和语义的越来越多，而解释方法对于理解其决定至关重要。闭塞是一种良好的方法，可提供有关离散语言数据的解释，例如通过从输入中删除语言单元并衡量对模型决策的影响。我们认为，当前基于闭塞的方法通常会产生无效或句法错误的语言数据，从而忽略了最近NLP模型的提高能力。此外，基于梯度的解释方法无视NLP中数据的离散分布。因此，我们提出了OLM：一种新颖的解释方法，将闭塞和语言模型结合在一起，以在原始输入的背景下以高可能性进行有效和句法校正替换。我们奠定了一个理论基础，该基础减轻了NLP中其他解释方法的这些弱点，并提供了在基于闭塞的解释中考虑数据可能性的重要性的结果。
Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on discrete language data, e.g. by removing a language unit from an input and measuring the impact on a model's decision. We argue that current occlusion-based methods often produce invalid or syntactically incorrect language data, neglecting the improved abilities of recent NLP models. Furthermore, gradient-based explanation methods disregard the discrete distribution of data in NLP. Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input. We lay out a theoretical foundation that alleviates these weaknesses of other explanation methods in NLP and provide results that underline the importance of considering data likelihood in occlusion-based explanation.
