%0 Journal Article %1 zaidan2007uar %A Zaidan, O.F. %A Eisner, J. %A Piatko, C.D. %D 2007 %J Proceedings of NAACL HLT %K classification text %P 260--267 %T Using “Annotator Rationales” to Improve Machine Learning for Text Categorization %X We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting 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 training to boost performance significantly on a sample task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an annotator’s time than annotating more examples.

@article{zaidan2007uar, abstract = {We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting 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 training to boost performance significantly on a sample task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an annotator’s time than annotating more examples.}, added-at = {2007-05-30T19:12:51.000+0200}, author = {Zaidan, O.F. and Eisner, J. and Piatko, C.D.}, biburl = {https://www.bibsonomy.org/bibtex/203e17c5ec965a3dd089074036267c908/flawed}, description = {user gets queried for significant (word) features}, interhash = {ccaedafddcd19cd8f25ea204b11cafae}, intrahash = {03e17c5ec965a3dd089074036267c908}, journal = {Proceedings of NAACL HLT}, keywords = {classification text}, pages = {260--267}, timestamp = {2007-05-30T19:12:51.000+0200}, title = {{Using “Annotator Rationales” to Improve Machine Learning for Text Categorization}}, year = 2007 }