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    <abstract>We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.</abstract>
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%0 Conference Proceedings
%T Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
%A Bizzoni, Yuri
%A Ghanimifard, Mehdi
%Y Beigman Klebanov, Beata
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%S Proceedings of the Workshop on Figurative Language Processing
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F bizzoni-ghanimifard-2018-bigrams
%X We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.
%R 10.18653/v1/W18-0911
%U https://aclanthology.org/W18-0911
%U https://doi.org/10.18653/v1/W18-0911
%P 91-101

Markdown (Informal)

[Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection](https://aclanthology.org/W18-0911) (Bizzoni & Ghanimifard, Fig-Lang 2018)

• Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection (Bizzoni & Ghanimifard, Fig-Lang 2018)

ACL

• Yuri Bizzoni and Mehdi Ghanimifard. 2018. Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 91–101, New Orleans, Louisiana. Association for Computational Linguistics.