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    <abstract>We present new results for the problem of sequence metaphor labeling, using the recently developed Visibility Embeddings. We show that concatenating such embeddings to the input of a BiLSTM obtains consistent and significant improvements at almost no cost, and we present further improved results when visibility embeddings are combined with BERT.</abstract>
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%0 Conference Proceedings
%T Neural Metaphor Detection with Visibility Embeddings
%A Kehat, Gitit
%A Pustejovsky, James
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F kehat-pustejovsky-2021-neural
%X We present new results for the problem of sequence metaphor labeling, using the recently developed Visibility Embeddings. We show that concatenating such embeddings to the input of a BiLSTM obtains consistent and significant improvements at almost no cost, and we present further improved results when visibility embeddings are combined with BERT.
%R 10.18653/v1/2021.starsem-1.21
%U https://aclanthology.org/2021.starsem-1.21
%U https://doi.org/10.18653/v1/2021.starsem-1.21
%P 222-228

Markdown (Informal)

[Neural Metaphor Detection with Visibility Embeddings](https://aclanthology.org/2021.starsem-1.21) (Kehat & Pustejovsky, *SEM 2021)

• Neural Metaphor Detection with Visibility Embeddings (Kehat & Pustejovsky, *SEM 2021)

ACL

• Gitit Kehat and James Pustejovsky. 2021. Neural Metaphor Detection with Visibility Embeddings. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 222–228, Online. Association for Computational Linguistics.