Byron Wallace - Using rationales and influential training examples to (attempt to) explain neural predictions in NLP
From Katie Gentilello on September 10th, 2020
Modern deep learning models for natural language processing (NLP) achieve state-of-the-art predictive performance but are notoriously opaque. I will discuss recent work looking to address this limitation. I will focus specifically on approaches to: (i) Providing snippets of text (sometimes called "rationales") that support predictions, and; (ii) Identifying examples from the training data that influenced a given model output.