Abstract
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.