@InProceedings{liu-EtAl:2017:EMNLP20173,
  author    = {Liu, Pengfei  and  Qian, Kaiyu  and  Qiu, Xipeng  and  Huang, Xuanjing},
  title     = {Idiom-Aware Compositional Distributed Semantics},
  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     = {1204--1213},
  abstract  = {Idioms are peculiar linguistic constructions that impose great challenges for
	representing the semantics of language, especially in current prevailing
	end-to-end neural models, which assume that the semantics of a phrase or
	sentence can be literally composed from its constitutive words.
	In this paper, we propose an idiom-aware distributed semantic model to build
	representation of sentences on the basis of understanding their contained
	idioms. Our models are grounded in the literal-first psycholinguistic
	hypothesis, which can adaptively learn semantic compositionality of a phrase
	literally or idiomatically. To better evaluate our models, we also construct an
	idiom-enriched sentiment classification dataset with considerable scale and
	abundant peculiarities of idioms. The qualitative and quantitative experimental
	analyses demonstrate the efficacy of our models.
	Author{1}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1124}
}