@inproceedings{dankers-etal-2020-neighbourly,
title = "Being neighbourly: Neural metaphor identification in discourse",
author = "Dankers, Verna and
Malhotra, Karan and
Kudva, Gaurav and
Medentsiy, Volodymyr and
Shutova, Ekaterina",
editor = "Klebanov, Beata Beigman and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee and
Feldman, Anna and
Ghosh, Debanjan",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.figlang-1.31/",
doi = "10.18653/v1/2020.figlang-1.31",
pages = "227--234",
abstract = "Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence. However, a large body of corpus-linguistic research suggests that situational information and broader discourse properties influence metaphor production and comprehension. In this paper, we present the first neural metaphor processing architecture that models a broader discourse through the use of attention mechanisms. Our models advance the state of the art on the all POS track of the 2018 VU Amsterdam metaphor identification task. The inclusion of discourse-level information yields further significant improvements."
}
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%0 Conference Proceedings
%T Being neighbourly: Neural metaphor identification in discourse
%A Dankers, Verna
%A Malhotra, Karan
%A Kudva, Gaurav
%A Medentsiy, Volodymyr
%A Shutova, Ekaterina
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F dankers-etal-2020-neighbourly
%X Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence. However, a large body of corpus-linguistic research suggests that situational information and broader discourse properties influence metaphor production and comprehension. In this paper, we present the first neural metaphor processing architecture that models a broader discourse through the use of attention mechanisms. Our models advance the state of the art on the all POS track of the 2018 VU Amsterdam metaphor identification task. The inclusion of discourse-level information yields further significant improvements.
%R 10.18653/v1/2020.figlang-1.31
%U https://aclanthology.org/2020.figlang-1.31/
%U https://doi.org/10.18653/v1/2020.figlang-1.31
%P 227-234
Markdown (Informal)
[Being neighbourly: Neural metaphor identification in discourse](https://aclanthology.org/2020.figlang-1.31/) (Dankers et al., Fig-Lang 2020)
- Being neighbourly: Neural metaphor identification in discourse (Dankers et al., Fig-Lang 2020)
ACL
- Verna Dankers, Karan Malhotra, Gaurav Kudva, Volodymyr Medentsiy, and Ekaterina Shutova. 2020. Being neighbourly: Neural metaphor identification in discourse. In Proceedings of the Second Workshop on Figurative Language Processing, pages 227–234, Online. Association for Computational Linguistics.