Chinese Lexical Substitution: Dataset and Method

Jipeng Qiang, Kang Liu, LiYing, Yun Li, Yi Zhu, Yun-Hao Yuan, Xiaocheng Hu, Xiaoye Ouyang

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Semantics: Lexical

Keywords: Lexical substitution, Chinese writing assistance, Substitution generation

Abstract: Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on human and machine collaboration. Based on our annotation method, we construct the first Chinese LS dataset CHNLS which consists of 33,695 instances and 144,708 substitutes, covering three text genres (News, Novel, and Wikipedia). Specifically, we first combine four unsupervised LS methods as an ensemble method to generate the candidate substitutes, and then let human annotators judge these candidates or add new ones. This collaborative process combines the diversity of machine-generated substitutes with the expertise of human annotators. Experimental results that the ensemble method outperforms other LS methods. To our best knowledge, this is the first study for the Chinese LS task.

Submission Number: 3500