Paper: Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization

ACL ID N07-1033
Title Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

We propose a new framework for supervised ma- chine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “ra- tionales.” When annotating an example, the hu- man teacher will also highlight evidence support- ing this annotation—thereby teaching the machine learner why the example belongs to the category. We provide some rationale-annotated data and present a learning method that exploits the rationales during trainingtoboostperformancesignificantlyonasam- ple task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an an- notator’s time than annotating more examples.