Chinese Lexical Simplification
This work addresses the problem of text readability for children and non-native speakers in Chinese, but it is incremental as it establishes a new benchmark rather than advancing state-of-the-art methods.
The authors tackled the lack of research in Chinese lexical simplification by creating the first benchmark dataset for this task and evaluating five baseline methods, including synonym-based, word embedding-based, pretrained language model-based, sememe-based, and hybrid approaches, to generate substitute candidates for complex words.
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. In order to acquire more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that include synonym-based approach, word embedding-based approach, pretrained language model-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task.