Automatically learning human rationales for machine learning / Spring 2019 - Master Thesis
Previous research has shown that enriching labels for classification tasks such as sentiment analysis with human annotators’ “rationales” can produce substantial improvements in classification performance. Moreover, in low-resource settings these rationales can be used to learn attention . Human rationales can be annotated manually or can be learned automatically . We want to combine these two methods by learning the rationales directly from human brain activity data .
This MSc thesis project includes developing a method to extract passive human rationales from eye-tracking and electroencephalography (EEG) data and building a machine learning system for tasks such as sentiment analysis which evaluates the quality of the rationales.
Supervised by Nora Hollenstein
-  Bao, Y. et al. "Deriving Machine Attention from Human Rationales." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
-  Yessenalina, A., Y. Choi, and C. Cardie. "Automatically generating annotator rationales to improve sentiment classification." Proceedings of the ACL 2010 Conference Short Papers. Association for Computational Linguistics, 2010.
-  Hollenstein N., Rotsztejn J., Troendle M., Pedroni A., Zhang C. and Langer N. “ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading.” Scientific Data. 2018.