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Our Focus: Natural Language Explanations
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Teaching Machines Through Human Explanations
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- 1 Intro
- 2 A Surprisingly "Simple" Recipe for Modern NLP
- 3 Cost of data labeling: relation extraction
- 4 Cost of data labeling: more complex task
- 5 Workaround for (less) data labeling?
- 6 How "labels" alone could make things wrong
- 7 From "labels" to "explanations of labels" One explanation generalizes to many examples
- 8 Learning from Human Explanation
- 9 Our Focus: Natural Language Explanations
- 10 Learning with Human Explanations
- 11 Explanations to "labeling rules"
- 12 Generalizing explanations Matching labeling rules to create pseudo labeled data
- 13 Challenge: Language Variations
- 14 Neural Rule Grounding for rule generalization
- 15 A Learable, Soft Rule Matching Function
- 16 Neural Execution Tree (NEXT) for Soft Matching
- 17 Study on Label Efficiency (TACRED)
- 18 Results: Hate Speech (Binary) Classification
- 19 Take-aways . "One explanation generalizes to many examples" - better label efficiency vs. conventional supervision
