Masked Metaphor Modeling To Transfer Literal to Metaphorical Text

This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models. Unlike similar studies, the proposed algorithm is not limited to the replacement of verbs, but also of nouns and adjectives. Despite the fact that the transfer rate for the former is the highest (56%), the transfer of the latter is feasible (24% and 31%). Human evaluation showed that our system-generated metaphors are considered more creative and metaphorical than human-generated ones. Additionally, when using our transferred metaphors for data augmentation we show that state of the art metaphorical sentence classification improves by 3% in F1.

On the Impact of Temporal Representations on Metaphor Detection

State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using sequential metaphor classifiers based on neural networks. The signal that represents the literal meaning is often represented by (non-contextual) word embeddings. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This rises the question whether different, possibly time-specific, representations of literal meanings may impact on the metaphor detection task. To the best of our knowledge, this is the first study which examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned to different state-of-the-art approaches. The results suggest that different word embeddings do impact on the metaphor detection task and some temporal word embeddings slightly outperform static methods on some performance measures. However, results also suggest that temporal word embeddings may provide representations of words' core meaning even too close to their metaphorical meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.