PIE : A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing (2021)

Abstract: Idiomatic expressions (IE) play an important role in natural language, and have long been a “pain in the neck” for NLP systems. Despite this, text generation tasks related to IEs remain largely under-explored. In this paper, we propose two new tasks of idiomatic sentence generation and paraphrasing to fill this research gap. We introduce a curated dataset of 823 IEs, and a parallel corpus with sentences containing them and the same sentences where the IEs were replaced by their literal paraphrases as the primary resource for our tasks. We benchmark existing deep learning models, which have state-of-the-art performance on related tasks using automated and manual evaluation with our dataset to inspire further research on our proposed tasks. By establishing baseline models, we pave the way for more comprehensive and accurate modeling of IEs, both for generation and paraphrasing.

## introduction idiomatic expressions (ies) make language natural. these are multiword expressions (mwes) that are non-compositional because their meaning differs from the literal meaning of their constituent words taken together @xcite . their use imparts naturalness and fluency @xcite @xcite , is prompted by pragmatic and topical functions in discourse @xcite and often conveys a nuance in expression (stylistic enhancement) using imagery that is beyond what is available in the context @xcite . idiomatic expressions, including phrasal verbs (e.g., carry out), idioms (e.g., pull one's leg) are also an essential part of a native speakers vocabulary and lexicon @xcite . ies constitute a ubiquitous part of daily language and social communication, primarily used in conversation, fiction and news @xcite , frequently used by teachers when presenting their lessons to students @xcite and occur cross-lingually @xcite . their non-compositionality is the reason for their classical standing as "a pain in the neck" @xcite and "hard going" @xcite for nlp. the oxford english dictionary defines the phrasal verb (an ie) vote out as 'to turn (a person) out of office.' using google translate foot_1 to translate the topical slogan "vote them out!" into eight of the world's most spoken and relatively resource-rich languages yielded the results shown in figure 1 . as native speakers will attest, other than in spanish, all the translations mean just the opposite, "vote for them!" this, and other studies on computational processing of idioms and metaphors in @xcite @xcite reinforce the need for nuanced language processing-a grand challenge for nlp systems. gaining a deeper understanding of ies and their literal counterparts is an important step toward this goal. in this paper, we introduce two novel tasks related to paraphrasing between literal and idiomatic expressions in unrestricted text: (1) idiomatic sentence simplification (iss) to automatically paraphrase idiomatic expressions in text, and 2) idiomatic sentence generation (isg) to replace a literal phrase in a sentence with a synonymous but more vivid phrase (e.g., an idiom). iss directly addresses the need for performing text simplification in several application settings, including summarizers @xcite and parsing @xcite . moreover, iss may actually be helpful when an idiomatic expression does not have an exact counterpart in a target language. this is akin to the 'translation by paraphrase' strategy recommended for human translation when the source language idiom is obscure and non-existent in the target language @xcite . on the other hand, isg advances the area of text style transfer @xcite bringing the as yet unexplored dimension of nuanced language to style transfer. a second important component of this paper is the introduction of a new curated dataset of parallel idiomatic and literal sentences, where the idiomatic expressions are paraphrased, created for the purpose of advancing progress in nuanced language processing and serving as a testbed for the proposed tasks. recent literature has explored several aspects of figurative and nonliteral language processing, including detecting and interpreting metaphors @xcite , disambiguating ies for their figurative or literal in a given context @xcite @xcite and analyzing sarcasm @xcite @xcite , by using curated datasets of sentences with linguistic processes in the wild. these datasets are ill-suited for the proposed tasks because they consist of specific figurative constructions (metaphors) @xcite , do not cover multiple ies @xcite , or are not parallel @xcite underscoring the need for a new dataset. the newly constructed dataset permits us to benchmark the performance of several state-ofthe-art neural network architectures (seq2seq and pretrained+fine-tuned models, with and without copy-enrichment) that have demonstrated compet-itive performance in the related tasks of simplification, and style transfer. using automatic and manual evaluations of the outputs for the two tasks, we find that the existing models are inadequate for the proposed tasks. the sequence-to-sequence models clearly suffer from data sparsity, the added copy mechanism helps preserve the context that is not replaced, and despite their prior knowledge of the pretrained models, they are still limited in their ability to paraphrase and generate. this leads us to discussing novel insights, applications and future directions for related research. the main contributions of this work are summarized as follows. ## task definition we propose two new tasks: idiomatic sentence generation transforms a literal sentence into a sentence involving idioms. used frequently in everyday language, idioms are known to add color to expressions and improve the fluency of communication. the idiomatic rewriting improves the quality of text generation in that it could enhance the textual diversity and convey abstract and complicated ideas in a succinct manner. for example, the idiomatic sentence bp cut corners and violated safety requirements conveys the same idea as its literal counterpart bp saved time, money and energy and violated safety requirements, but in a more vivid and succinct manner. the second task is idiomatic sentence paraphrasing, simplifying sentences with idioms into literal expressions. as an example, the sentence-it is certainly not a sensible move to cut corners with national securityhas the idiom cut corners replaced the literal counterpart save money. by paraphrasing the idioms from which machine translation often suffers, our task of idiomatic sentence paraphrasing can also benefit machine translation. in this work, we distinguish our task of idiomatic sentence generation from idiom generation. while the latter task creates new idioms with novel word combinations, our study is to use existing idioms in a sentence and preserve the semantic meaning. the task of idiomatic sentence paraphrasing is closely related to text simplification that has mostly been studied as related tasks of lexical paraphrasing and syntactic paraphrasing @xcite . a significant departure of this task from that of these related tasks that centrally address style is that (i) we aim for local synonymous paraphrasing by transforming not the entire sentence but a phrase in the sentence, (ii) the transformation is not related to syntactic structures, but related to the complexity in meaning foot_2 . we propose doing joint monolingual translation with simplification and is similar in spirit to @xcite . there are many technical challenges to performing these tasks. the task of idiomatic sentence paraphrasing involves first identifying that an expression is an idiom and not a literal expression (e.g. black sheep) @xcite @xcite . once identified, the ie may have multiple senses (e.g. tick off ) and its appropriate sense will need to be identified before paraphrasing it. third, an appropriate literal phrase will have to be generated to replace the ie. finally, the literal phrase will have to be fit in the surrounding sentential context for a fluent construction. for idiomatic sentence generation, the context of the literal phrase could permit more than one candidate idiom (e.g. keep quiet). in this study, we assume that we have an idiomatic sentence and leave it to future work to explore the task in conjunction with this step. ## related work the theme of this paper is naturally connected to three streams of text generation tasksparaphrasing, style transfer and metaphoric expression generation. we will discuss these tasks and also the datasets used in these tasks to study their similarities and differences to our dataset and tasks. ## building the dataset we describe the details of the data collection, data annotation, corpus analyses and comparisons with other existing corpora. ## results and discussion results. we report the automatic and human evaluation results in when it comes to the comparison between bart and the pipeline, bart does better in retaining the contexts surrounding idiomatic expressions given its high context score in human evaluation while the pipeline is better at handling the idiomatic part, i.e., target inclusion. despite the reported superior performance of bart in related text generation tasks @xcite , our experiments show that bart has limited capability in idiom paraphrasing and generation. the pipeline method, by virtue of error propagation from its retrieval and deletion modules suffers in terms of both the context preservation and fluency. for task of idiomatic sentence generation, the accuracy for retrieval module is 0.27 and f1 score for deletion module is 0.68. for task of idiomatic sentence paraphrasing, the accuracy for retrieval module is 0.96 and f1 score for deletion module is 0.85. comparison between two tasks. according to human evaluation results in