IEKG : A Commonsense Knowledge Graph for Idiomatic Expressions (2023)

Abstract: Idiomatic expression (IE) processing and comprehension have challenged pre-trained language models (PTLMs) because their meanings are non-compositional. Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs. This extends the established {ATOMIC}_{20}^{20} converting PTLMs into knowledge models (KMs) that encode and infer commonsense knowledge related to IE use. Experiments show that various PTLMs can be converted into KMs with IEKG. We verify the quality of IEKG and the ability of the trained KMs with automatic and human evaluation. Through applications in natural language understanding, we show that a PTLM injected with knowledge from IEKG exhibits improved IE comprehension ability and can generalize to IEs unseen during training.

## introduction idiomatic expressions (ies) are frequently used in natural language and include a variety of versatile figures of speech that improve language fluency and conciseness in multiple genres @xcite @xcite . prior nlp research in ie processing has focused on detecting idiomaticity ( @xcite among others), including tasks and applications that require ie comprehension, e.g., sentiment classification @xcite , machine translation @xcite , natural language inference @xcite , and dialog systems @xcite . ie comprehension poses challenges to nlp systems @xcite @xcite mainly owing to their failure to account for ies' characteristic non-compositionality, i.e., the meaning of an expression is not derivable from the mean-ings of its components @xcite . for example, consider the case of a sentiment classifier that incorrectly recognizes the negative sentiment in the statement they have stirred up a hornet's nest foot_0 . by failing to account for the figurative interpretation of the ie @xcite . this study focuses on injecting ie-related knowledge into small-frame ptlms known for their wide use, such as bert @xcite and bart @xcite , considering their struggle to understand the figurative meanings of ies @xcite . we discuss the corresponding capabilities of large ptlms, such as gpt-3.5, in the limitation section. towards enabling ie comprehension, prior efforts range from automatic idiom-detection methods @xcite @xcite to learning ie representations, all using sentences with ies (i.e., idiomatic sentences), where ie-specific information would be learned implicitly from limited samples and sparse contextual information @xcite . for instance, magpie @xcite , the largest-todate corpus for ies, spans 1,755 distinct ies, where a staggering 81% of them each have less than 50 idiomatic sentences (ies are individually rare in natural language), and certain idiomatic sentences therein contain little to no contextual information to allow models to learn the ies' meanings (e.g., the thing was on the blink again.). as such, prior work on ie embedding learning relied on the auxiliary aid of ie dictionary definitions to augment the information available in idiomatic sentences @xcite ). an alternative approach of explicitly learning commonsense knowledge about ies without significantly scaling the data/parameters constitutes the crux of this study. we rely on psycholinguistic findings about the impact of ie-related aspects, such as mental states, emotions, and likely x licks y's boots x is pathetic x is insincere x is obsessive attributes of x x is seen as effects on y annoyed distance themselves from x effects on x y to reciprocate a way to convince y easily do whatever y asks causes of x to gain a favor with y y to help them desperate try to prove their loyalty as a result, x feels as a result, x will as a result, x wants someone else to follow them other than x x to stop because x wanted as a result, y wants as a result, y will as a result, y feels figure 1: example ie lick someone's boot in iekg. hence, we construct the idiomatic expression knowledge graph (iekg), an ie-centered extension to the atomic 20 20 kg, that serves as an instance-efficient and explicit ie-related source of knowledge (compared to the implicit knowledge available in a large number of idiomatic sentences) for ie processing. this permits a novel exploration of how ie-related knowledge from a kg can be used for ie-related comprehension in parameterefficient and sample-limited scenarios. iekg follows the schema of atomic 20 20 (see figure 1 ) and organizes the idiomatic interpretations for ies into reasoning types covering the ef-fects, causes, and attributes for both the subject and the object in an ie (see section 3). working closely with human annotators that created 56,315 instances for 1,229 idiomatic events and 11 relation types, iekg's quality and diversity were then ascertained via human and automatic evaluation. we exhibit its wide utility via (1) neural knowledge models (kms) trained on iekg to show iekg equips ptlms with ie knowledge that is generalizable to out-of-graph instances; and (2) ie knowledge injection to showcase the usefulness of iekg for ie comprehension tasks, such as natural language inference (nli) with ies and continuation classification in which the model decides if a continuation is appropriate for a given context with an ie. the contributions of this work are as follows. (1) we propose iekg, a commonsense knowledge graph focusing on idiomatically interpreted ies; commonsense knowledge graphs. knowledge graphs organize information into an ontology as a multi-relational graph of edges and entity nodes @xcite . commonsense kgs, such as atomic @xcite , conceptnet @xcite , cskg @xcite , aser @xcite , and transomcs @xcite , are a subset of kgs that focus on generic world commonsense knowledge. specifically, atomic (and its successor atomic 20 20 @xcite ) utilizes if-then relations to describe rich causal and inferential knowledge spanning several dimensions, including causes vs. effects, agents vs. themes, and voluntary vs. involuntary events. atomic has also been used in figurative language-related applications, such as interpretation and generation of figurative language (e.g., simile, idioms, and metaphor) @xcite . however, as mentioned in section 1, atomic 20 20 's coverage of ies is limited. through the current work, iekg extends atomic 20 20 's representation to idiomatic events to further ie-related applications. ## idiomatic expression knowledge graph by design, iekg aligns with atomic 20 20 's general structure and relation types to describe the linguistic knowledge of ie uses so that it enables commonsense inference on figurative language. ## neural knowledge model for ies we experiment with kg/ptlm incorporation by training kms on iekg and comparing their capability in processing idiomatic events to the km trained on atomic 20 20 to showcase the additional ie knowledge that can be learned from iekg. ## applications we show how iekg enables ie comprehension through two applications that require a model to understand ies' semantics to perform correctly with their largest and most recent benchmark datasets. ## conclusion we propose iekg, a knowledge graph that describes the commonsensical, figurative interpretation of ies. we show various ptlms can be converted into kms via the tuple completion task by training on iekg and iekg imparts useful utility for tasks that require ie comprehension. future work should extend iekg to disambiguate between literal and figurative ie interpretations, and investigate factors that affect the models' generalizability to ies not covered by iekg.