We present end-to-end neural models for detecting metaphorical word use in context. We
show that relatively standard BiLSTM models which operate on complete sentences work …

Metaphors are an integral and important part of human communication and greatly impact
the way our thinking is formed and how we understand the world. The theory of the …

As the community working on computational approaches to figurative language is growing
and as methods and data become increasingly diverse, it is important to create widely …

Most current approaches to metaphor identification use restricted linguistic contexts, eg by
considering only a verb's arguments or the sentence containing a phrase. Inspired by …

This paper describes systems submitted to the Metaphor Shared Task at the Second
Workshop on Figurative Language Processing. In this submission, we replicate the …

This study presents a new approach to metaphorical paraphrase generation by masking
literal tokens of literal sentences and unmasking them with metaphorical language models …

We present new results on Metaphor Detection by using text from visual datasets. Using a
straightforward technique for sampling text from Vision-Language datasets, we create a data …

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 …

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 …

Studies identifying metaphors are assessed per word as a scoring scheme for the
effectiveness of the demonstrated methods. This scoring poses a problem in the validity of …