@inproceedings{liu-etal-2017-idiom,
    title = "Idiom-Aware Compositional Distributed Semantics",
    author = "Liu, Pengfei  and
      Qian, Kaiyu  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D17-1124/",
    doi = "10.18653/v1/D17-1124",
    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."
}