To overcome the sparsity of KBs, knowledge models such as COMET (Bosselut et al., 2019 Hwang et al., 2021) fine-tuned an LM on structured KB triplets. Notably, ConceptNet (Speer et al., 2017) is a large-scale concept- centric KB, while ATOMIC (Sap et al., 2019) contains event-centric knowledge about causes, effects, and the mental states of the participants. To that end, commonsense knowledge bases (KBs) record such facts. Many language tasks require relying on implicit commonsense knowledge that is never mentioned explicitly because it is assumed to be known by everyone. With respect to novel similes and metaphors, they are interpreted through shared literal attributes between the topic and vehicle (e.g., “Antarctica is cold, can a house also be cold?”) (Wolff and Gentner, 2000 Carston and Wearing, 2011). Asl ( 2013) repeated the same experiment with stories, and concluded that longer contexts improved people’s ability to interpret unknown idioms. For example, a participant asked to interpret “robbing the cradle” in the context “Robert knew that he was robbing the cradle by dating a sixteen-year-old girl” used the literal meaning of cradle to associate the meaning with babies and indirectly with young age, and along with the context inferred that it meant to “date a very young person”.

He found that the leading strategy was to infer the meaning from the given context, which led to successful interpretation 57% of the time, followed by relying on the literal meaning of the constituent words (22% success rate). Cooper ( 1999) studied how L2 English speakers interpret unfamiliar English idioms. The ways in which humans process figurative language may inspire computational work on figurative language interpretation. 2.3 Human Processing of Figurative Language ( 2021a) showed that state-of-the-art NLI models fail on pragmatic inferences involving similes. ( 2021) proposed methods for generating similes from their literal counterparts, while Chakrabarty et al. In other lines of work, Chakrabarty et al. Simile interpretation focused on inferring the implicit property (Qadir et al., 2016). Earlier work relied on semantic and syntactic characteristics, namely, higher semantic similarity between the topic and the vehicle in literal comparisons than in figurative comparisons (Niculae and Danescu-Niculescu-Mizil, 2014 Qadir et al., 2015 Mpouli, 2017), and dictionary definitions (Qadir et al., 2016), while more recent work is based on neural methods (Liu et al., 2018 Zeng et al., 2020). Most work in NLP has focused on simile detection, that is, distinguishing literal from figurative comparisons. Similes may either be explicit, namely, specify the topic, vehicle, and similarity property, as in “The house was cold like Antarctica” (where the topic is “house”, the vehicle is “Antarctica” and the property of comparison is “cold”), or implicit, namely, omitting the property, as in “the house was like Antarctica” ( Section 3.2). Similes are a figure of speech that compares two things, usually with the intent to make the description more emphatic or vivid, and spark the reader’s imagination (Paul et al., 1970). The knowledge-enhanced models improve the performance on both the discriminative and generative tasks, further bridging the gap from human performance. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language types: inferring meaning from the context and relying on the constituent words’ literal meanings. Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We then trained models to choose or generate the plausible continuation. We collected datasets of fictional narratives containing a figurative expression along with crowd-sourced plausible and implausible continuations relying on the correct interpretation of the expression. In this paper, we study the interpretation of two non-compositional figurative languages (idioms and similes). Existing text representations by design rely on compositionality, while figurative language is often non- compositional.

Yet, the vast majority of NLP research focuses on literal language. Figurative language is ubiquitous in English.

Figurative language inside the office