A BERT-based two-stage model for Chinese Chengyu recommendation

• Main Authors: TAN, Minghuan, Jing JIANG, DAI, Bingtian
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: natural language processing; chengyu recommendation; idiom understanding; question answering; Databases and Information Systems; East Asian Languages and Societies; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5821; https://ink.library.smu.edu.sg/context/sis_research/article/6824/viewcontent/ink_Chinese_Idiom_Prediction__TALLIP___nonacm_.pdf
• Institution: Singapore Management University

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on ChID and CCT datasets and find that it can achieve the state of the art on both datasets. Ablation studies show that both stages of training are critical for the performance gain.