Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.
|Title of host publication||Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics|
|Place of Publication||Melbourne, Australia|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||10|
|Volume||1: Long Papers|
|Publication status||Published - 17 Jul 2018|
|Event||The 56th Annual Meeting of the Association for Computational Linguistics - Melbourne, Australia|
Duration: 15 Jul 2018 → 20 Jul 2018
|Conference||The 56th Annual Meeting of the Association for Computational Linguistics|
|Period||15/07/18 → 20/07/18|
- Metaphor Identification
- Metaphor Interpretation
- Word Embedding output vector
Mao, R., Lin, C., & Guerin, F. (2018). Word Embedding and WordNet Based Metaphor Identification and Interpretation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers, pp. 1222-1231). Melbourne, Australia: Association for Computational Linguistics (ACL).