Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks

Jennifer Brooks, Abdou Youssef


Abstract
In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: “nouns”, “verbs”, “adverbs/adjectives”, or “other”. The models were combined into voting pools and the voting pools were combined using the logical “OR” operator.
Anthology ID:
2020.figlang-1.33
Volume:
Proceedings of the Second Workshop on Figurative Language Processing
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
244–249
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.33
DOI:
10.18653/v1/2020.figlang-1.33
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.33.pdf
Video:
 http://slideslive.com/38929728