Idiomatic Expression Identification using Semantic Compatibility

Abstract

Idiomatic expressions are an integral part of natural language and constantly being added to a language. Owing to their noncompositionality and their ability to take on a figurative or literal meaning depending on the sentential context, they have been a classical challenge for NLP systems. To address this challenge, we study the task of detecting whether a sentence has an idiomatic expression and localizing it when it occurs in a figurative sense. Prior art for this task had studied specific classes of idiomatic expressions offering limited views of their generalizability to new idioms. We propose a multistage neural architecture with attention flow as a solution. The network effectively fuses contextual and lexical information at different levels using word and sub-word representations. Empirical evaluations on three of the largest benchmark datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality show that our proposed model achieves new state-of-the-art results. A salient feature of the model is its ability to identify idioms unseen during training with gains from 1.4% to 30.8% over competitive baselines on the largest dataset.

Idiomatic Expression Paraphrasing without Strong Supervision

“…Table 1 shows examples of idiomatic and literal sentences between which we expect to paraphrase. Ideally, an ISP system would have an IE span detection stage to detect the presence and span of IEs (Zeng and Bhat 2021) and feeds only idiomatic sentences to ISP. Here we study the ISP task on its own and assume the input sentence is idiomatic and the IE span is available.…”

“…IE processing tasks consider idiom type classification and idiom token classification (Liu 2019): idiom type classification (Cordeiro et al 2016) determines if a phrase could be used as an IE; and idiom token classification (Liu andHwa 2017, 2019) disambiguates if a given potentially idiomatic expression is used literally or idiomatically in a given context (sentence). Most prior works require the knowledge of the IE (Liu andHwa 2017, 2019) but recent efforts on idiom span detection (Zeng and Bhat 2021) have removed the need for IEs' identity. Our study is in line with the traditional set-up where the IE positions are assumed to be known.…”