Idiomatic Expression Identification using Semantic Compatibility (2021)

Abstract: Idiomatic expressions are an integral part of natural language and constantly being added to a language. Owing to their non-compositionality 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 research for this task has studied specific classes of idiomatic expressions offering limited views of their generalizability to new idioms. We propose a multi-stage 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.

## introduction idiomatic expressions (ies) are a special class of multi-word expressions (mwes) that typically occur as collocations and exhibit semantic noncompositionality (a.k.a. semantic idiomaticity), where the meaning of the expression is not derivable from its parts @xcite . in terms of occurrence, ies are individually rare, but collectively frequent in and constantly added to natural language across different genres @xcite . additionally, they are known to enhance fluency and used to convey ideas succinctly when used in everyday language @xcite . classically regarded as a ''pain in the neck'' to idiom-unaware nlp applications @xcite these phrases are challenging for reasons including their non-compositionality (semantic idiomaticity), besides taking a figurative or literal meaning depending on the context (semantic ambiguity), as shown by the example in despite being well-studied in the current literature as idiom type and token classification (e.g., @xcite @xcite @xcite , previous methods are limited for various reasons. they rely on knowing the pies being classified and hence their exact input tom said many bad things about jane behind her back. (figurative) he took one from an armchair and put it behind her back. (literal) output behind her @xmath0: example input and output for disc framework. when a potentially idiomatic expression (pie; italicized) is used idiomatically, the pie is identified and extracted as the output; otherwise the model outputs the start (<@xmath1. an effective solution to this task calls for the ability to relate the meaning of its component words with each other (e.g., @xcite as well as with the context @xcite . this aligns with the widely upheld psycholinguistic findings on human processing of a phrase's figurative meaningin comparison with its literal interpretation @xcite . toward this end, we rely on the contextualized representation of a pie (accounting both for its internal and contex-tual properties), hypothesizing that a figurative expression's contextualized representation should be different from that of its literal counterpart. we refer to this as its semantic compatibility (sc)-if a pie is semantically compatible with its context, then it is literal; if not, it is figurative. the idea of sc also captures the distinction between literal word combinations and idioms, in terms of the semantics encoded by both @xcite and the related property of selectional preference @xcite -the tendency for a word to semantically select or constrain which other words may appear in its association @xcite successfully used for processing metaphors @xcite and word sense disambiguation @xcite . we capture sc by effectively fusing information from the input tokens' contextualized and literal word representations to then localize the span of pie used figuratively. here we leverage the idea of attention flow previously studied in a machine comprehension setting @xcite . our main contributions in this work are: a novel ie identification model, disc, that uses attention flow to fuse lexical semantic information at different levels and discern the sc of a pie. taking only a sentence as input and using only word and pos representations, it simultaneously performs detection and localization of the pies used figuratively. to the best of our knowledge, this is the first such study on this task. ## method in line with studies on mwe identification mentioned above, we frame the identification of idiomatic subsequences as a token-level tagging problem, where we perform literal/idiomatic clas-sification for every token in the sentence. a simple post-processing step finally extracts the pie subsequence used in the idiomatic sense. ## results and analyses ie identification performance. a comparative evaluation of the models on the magpie, sem-eval5b, and vnc datasets is shown in overall, disc is the best performing model among all baseline models. specifically, disc and rnn-mhca show competitive results in all random split settings, however, disc has stronger performance on type-aware settings, indicating that the sc check enables disc to recognize the non-compositionality of idioms permitting it to generalize better to idioms unseen in the training set. therefore, while rnn-mhca might be as good as disc when it comes to identifying (and potentially memorizing) known idioms, disc is more capable of identifying unseen idioms since it better leverages the sc property of idioms in addition to memorization. in the random setting, disc performs on par with rnn-mhca and bert-bilstm-crf in terms of f1 and sa for magpie while outperforming all baselines using the other datasets. it is notable that even with the ability to perfectly localize pies, gazetteer has a low sa compared to the other top-performing models due to its inability to use the context to determine if the pie is used idiomatically. in the type-aware setting, the f1 of disc is comparable to that of rnn-mhca and bert-bilstm. however, in terms of sa, disc outperforms all models across all datasets. we also observe that for all datasets, achieving high f1 scores is much easier than achieving high sa. this is especially salient in the magpie type-aware split where all the models achieve similar f1s, whereas disc outperforms the others in terms of sa by margins ranging from 7% to 30.8% absolute points. moreover, gazetteer is unable to perform pie localization at all in this setting on accounts of its being limited to the instances available in an idiom lexicon. for magpie random split, it is notable that all the models (including gazetteer with its majority-class prediction) achieve at least 86% f1 score. for magpie type-aware split, disc performances marked with asterisks are comparable in their differences are not statistically significant at @xmath2.05 using bootstrapped samples that are estimated 10 5 times. ## conclusion and future work in this work, we studied how a neural architecture that fuses multiple levels of syntactic and semantic information of words can effectively perform idiomatic expression identification. compared to competitive baselines, the proposed model yielded state-of-the-art performance on pies that varied with respect to syntactic patterns, degree of compositionality and syntactic flexibility. a salient feature of the model is its ability to generalize to pies unseen in the training data. although the exploration in this work is limited to ies, we made no idiom-specific assumptions in the model. future directions should extend the study to nested and syntactically flexible pies (verbal mwes) and other figurative/literal constructions such as metaphors-categories that were not sufficiently represented in the datasets considered in this study. other concrete research directions include performing the task in crossand multi-lingual settings.