ProtoTEx: Explaining Model Decisions with Prototype Tensors

• URL: http://arxiv.org/abs/2204.05426v2
• Date: Mon, 23 May 2022 00:34:37 GMT
• Title: ProtoTEx: Explaining Model Decisions with Prototype Tensors
• Authors: Anubrata Das and Chitrank Gupta and Venelin Kovatchev and Matthew Lease and Junyi Jessy Li
• Abstract summary: ProtoTEx is a novel white-box NLP classification architecture based on prototype networks. We describe a novel interleaved training algorithm that effectively handles classes characterized by the absence of indicative features. On a propaganda detection task, ProtoTEx accuracy matches BART-large and exceeds BERT-large with the added benefit of providing faithful explanations.
• Score: 27.779971257213553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks. ProtoTEx faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples. At inference time, classification decisions are based on the distances between the input text and the prototype tensors, explained via the training examples most similar to the most influential prototypes. We also describe a novel interleaved training algorithm that effectively handles classes characterized by the absence of indicative features. On a propaganda detection task, ProtoTEx accuracy matches BART-large and exceeds BERT-large with the added benefit of providing faithful explanations. A user study also shows that prototype-based explanations help non-experts to better recognize propaganda in online news.

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