Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German

Abstract

In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors rely- ing only on word embeddings. The latter en- ables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the ad- jective word vector and the noun word vec- tor represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating arti- ficial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capa- bilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.

Tasks

Reproductions