Contextual Modulation for Relation-Level Metaphor Identification

Abstract

Identifying metaphors in text is very challenging and requires comprehending the underlying comparison. The automation of this cognitive process has gained wide attention lately. However, the majority of existing approaches concentrate on word-level identification by treating the task as either single-word classification or sequential labelling without explicitly modelling the interaction between the metaphor components. On the other hand, while existing relation-level approaches implicitly model this interaction, they ignore the context where the metaphor occurs. In this work, we address these limitations by introducing a novel architecture for identifying relation-level metaphoric expressions of certain grammatical relations based on contextual modulation. In a methodology inspired by works in visual reasoning, our approach is based on conditioning the neural network computation on the deep contextualised features of the candidate expressions using feature-wise linear modulation. We demonstrate that the proposed architecture achieves state-of-the-art results on benchmark datasets. The proposed methodology is generic and could be applied to other textual classification problems that benefit from contextual interaction.

Verb Metaphor Detection via Contextual Relation Learning

“…First, contextual information is mostly used to enhance the representation of the target word, but the interactions between the target word and its contexts are not explicitly modeled (Zayed et al, 2020;Su et al, 2020). To alleviate this, Su et al (2020) proposed a new paradigm by viewing metaphor detection as a reading comprehension problem, which uses the target word as a query and captures its interactions with the sentence and clause.…”

“…Song et al (2020) modeled metaphors as attribute-dependent domain mappings and presented a knowledge graph embedding approach for modeling nominal metaphors. Zayed et al (2020) identified verb-noun and adjective-noun phrasal metaphoric expressions by modeling phrase representations as a context.…”

“…However, sentential context is also useful but is ignored. In contrast, token-level metaphor detection explores wider context and gains improvements, but syntactic information is neglected and as discussed in (Zayed et al, 2020), interactions between metaphor components are not explicitly modeled.…”

“…More importantly, as shown in Figure 1, most token-level metaphor detection methods formulate metaphor detection as either a single-word classification or a sequence labeling problem (Gao et al, 2018). The context information is mainly used for learning contextual representations of tokens, rather than modeling the interactions between the target word and its contexts (Zayed et al, 2020).…”

Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification

“…Rei et al (2017) proposed a supervised similarity network for relation-level metaphor identification. Zayed et al (2020) introduced a novel architecture for identifying relation-level metaphoric expressions of certain grammatical relations based on contextual modulation, which achieved state-of-the-art results.…”