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

Felix Schneider, Sven Sickert, Phillip Brandes, Sophie Marshall, Joachim Denzler


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 relying only on word embeddings. The latter enables 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 adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial 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 capabilities 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.
Anthology ID:
2022.mwe-1.11
Volume:
Proceedings of the 18th Workshop on Multiword Expressions @LREC2022
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
MWE
SIG:
SIGLEX
Publisher:
European Language Resources Association
Note:
Pages:
75–80
Language:
URL:
https://aclanthology.org/2022.mwe-1.11
DOI:
Bibkey:
Cite (ACL):
Felix Schneider, Sven Sickert, Phillip Brandes, Sophie Marshall, and Joachim Denzler. 2022. Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German. In Proceedings of the 18th Workshop on Multiword Expressions @LREC2022, pages 75–80, Marseille, France. European Language Resources Association.
Cite (Informal):
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German (Schneider et al., MWE 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.mwe-1.11.pdf
Code
 cvjena/metaphor-detector