Idiomatic Expression Paraphrasing without Strong Supervision
Jianing Zhou, Ziheng Zeng, Hongyu Gong, Suma Bhat

TL;DR
This paper introduces unsupervised and weakly supervised methods for idiomatic sentence paraphrasing, effectively replacing idiomatic expressions with literal paraphrases without relying on large parallel corpora.
Contribution
It presents novel unsupervised and weakly supervised approaches for idiomatic paraphrasing, along with a new dataset and practical validation in machine translation.
Findings
Achieved over 5.16 BLEU point improvement
Improved METEOR by over 8.75 points
Enhanced SARI score by over 19.57 points
Abstract
Idiomatic expressions (IEs) play an essential role in natural language. In this paper, we study the task of idiomatic sentence paraphrasing (ISP), which aims to paraphrase a sentence with an IE by replacing the IE with its literal paraphrase. The lack of large-scale corpora with idiomatic-literal parallel sentences is a primary challenge for this task, for which we consider two separate solutions. First, we propose an unsupervised approach to ISP, which leverages an IE's contextual information and definition and does not require a parallel sentence training set. Second, we propose a weakly supervised approach using back-translation to jointly perform paraphrasing and generation of sentences with IEs to enlarge the small-scale parallel sentence training dataset. Other significant derivatives of the study include a model that replaces a literal phrase in a sentence with an IE to generate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsNatural Language Processing Techniques · Multimodal Machine Learning Applications · Topic Modeling
