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The general concept: we can figure out what lead to a particular classification for an item by finding smaller subsets of the item that still gave the same clas
by avivo 10y ago
The general concept: we can figure out what lead to a particular classification for an item by finding smaller subsets of the item that still gave the same classification.
For example, this can show which snippet of text implies a particular review should be classified as "very negative", or which part of an image lead to a classification of "cancerous" for a biopsy image.
This doesn't give you much predictive power about the network however, or tell you how it actually works in general. It simply tells you how it made a particular classification.
Paper link: https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf https://people.csail.mit.edu/taolei/papers/emnlp16_rationale...
- avivo 10y agoFrom the paper: "Our goal is to select a subset of the input sequence as a rationale. In order for the subset to qualify as a rationale it should satisfy two criteria: 1) the selected words should be interpretable and 2) they ought to suffice to reach nearly the same prediction (target vector) as the original input. In other words, a rationale must be short and sufficient."