Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5522–5526 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 5522 Abstract Although the proper use of idioms can enhance the elegance of writing, the active use of various expressions is a challenge because remembering idioms is difficult. In this study, we address the problem of idiom recommendation by leveraging a neural machine translation framework, in which we suppose that idioms are written in one pseudo target language. Two types of real- life datasets are collected to support this study. Experimental results show that the proposed approach achieves promising performance compared with other baseline methods. 1 Introduction Nearly every language has some ancient idioms, aphorisms, and sayings from history (Muzny et al., 2013; Moussallem et al., 2018). Chinese idioms, also known as “ready phrases” and usually consist of only four characters, can reveal complex meaning and enhance the conciseness and elegance of writing if properly used. For example, in the text segment “一夜春雷雨，朋友圈的微 商 如 雨 后 春 笋 般 冒 了 出 来 。 ” (During the thunderstorm overnight, microbusinessmen in the circle of friends sprang up like bamboo shoots after rain.), the author elegantly describes the rapid emergence of things in large numbers by properly using the popular idiom “雨后春笋 ” (When it rains in spring, many bamboo shoots grow simultaneously). Therefore, automatically recommending idioms that are pertinent to the input context is an appealing task because remembering idioms is difficult for most people. To this end, one typical and straightforward approach is to regard idiom recommendation as a standard classification problem and assign a piece of context to one idiom label by training corresponding classifiers. Whereas by doing so, the meaningful text information in the idiom itself tends to be ignored. Intuitively, combining textual information in the context and idiom in the training stage may be helpful. However, texts in the idiom are usually written in ancient classical Chinese for conciseness; thus, they are highly different from those in the context and difficult to directly utilize for classifying unseen contexts. In most cases, such as in the aforementioned example, few common words or characters are shared between the idiom and the surrounding context. In this study, we provide a new perspective for idiom recommendation by formulating it as a translation problem, in which the idioms are assumed to be written with a pseudo target language because they are usually written in ancient Chinese and have special and limited vocabularies. We propose a machine translation- based approach that operates in three stages. First, an attention-based neural network is used to encode the context sequence (source language). Second, the coded context attention vector is decoded into one intermediate sequence (target language). Third, the final recommended idioms are selected through sequence mapping. The remainder of this paper is organized as follows. The related work is surveyed in Section 2. Sections 3 and 4 present the proposed approach and experimental results, respectively. Finally, conclusions …

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