@InProceedings{yanaka-EtAl:2017:EMNLP2017,
  author    = {Yanaka, Hitomi  and  Mineshima, Koji  and  Mart\'{i}nez-G\'{o}mez, Pascual  and  Bekki, Daisuke},
  title     = {Determining Semantic Textual Similarity using Natural Deduction Proofs},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {681--691},
  abstract  = {Determining semantic textual similarity is a core research subject in natural
	language processing.
	Since vector-based models for sentence representation often use shallow
	information, capturing accurate semantics is difficult. By contrast, logical
	semantic representations capture deeper levels of sentence semantics, but their
	symbolic nature does not offer graded notions of textual similarity.
	We propose a method for determining semantic textual similarity by combining
	shallow features with features extracted from natural deduction proofs of
	bidirectional entailment relations between sentence pairs. For the natural
	deduction proofs, we use ccg2lambda, a higher-order automatic inference system,
	which converts Combinatory Categorial Grammar (CCG) derivation trees into
	semantic representations and conducts natural deduction proofs. Experiments
	show that our system was able to outperform other logic-based systems and that
	features derived from the proofs are effective for learning textual similarity.},
  url       = {https://www.aclweb.org/anthology/D17-1071}
}