Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling

• URL: http://arxiv.org/abs/2004.09890v1
• Date: Tue, 21 Apr 2020 10:37:44 GMT
• Title: Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
• Authors: David Harbecke, Christoph Alt
• Abstract summary: Occlusion is a well established method that provides explanations on discrete language data. We argue that current Occlusion-based methods often produce invalid or syntactically incorrect language data. We propose OLM: a novel explanation method that combines Occlusion and language models to sample valid and syntactically correct replacements.
• Score: 11.594541142399223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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