@InProceedings{hsu-chaudhary-samatova:2018:C18-1,
  author    = {Hsu, Shiou Tian  and  Chaudhary, Mandar  and  Samatova, Nagiza},
  title     = {Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1145--1155},
  abstract  = {Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20\% gain in rationale interpretability compared to state-of-the-art approaches.},
  url       = {http://www.aclweb.org/anthology/C18-1098}
}