End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
|Number of pages||11|
|Publication status||Published - Jul 2019|
|Event||57th Annual Meeting of the Association for Computational Linguistics (ACL) - Fortezza da Basso, Florence, Italy|
Duration: 28 Jul 2019 → 2 Aug 2019
|Conference||57th Annual Meeting of the Association for Computational Linguistics (ACL)|
|Period||28/07/19 → 2/08/19|
Mao, R., Lin, C., & Guerin, F. (2019). End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories. 3888-3898. Paper presented at 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy. https://doi.org/10.18653/v1/P19-1378