Learning Outside the Box: Discourse-level Features Improve Metaphor Identification
This addresses the problem of improving metaphor identification for NLP researchers, but it is incremental as it builds on existing methods with discourse-level features.
The paper tackled metaphor identification by incorporating broader discourse features, achieving near state-of-the-art results on the 2018 VU Amsterdam task without complex features or deep neural architectures.
Most current approaches to metaphor identification use restricted linguistic contexts, e.g. by considering only a verb's arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without the complex metaphor-specific features or deep neural architectures employed by other systems. A qualitative analysis further confirms the need for broader context in metaphor processing.