Lexical Complexity Controlled Sentence Generation for Language Learning

Language teachers spend a lot of time developing good examples for language learners. For this reason, we define a new task for language learning, lexical complexity controlled sentence generation, which requires precise control over the lexical complexity in the keywords to examples generation and better fluency and semantic consistency. The challenge of this task is to generate fluent sentences only using words of given complexity levels. We propose a simple but effective approach for this task based on complexity embedding while controlling sentence length and syntactic complexity at the decoding stage. Compared with potential solutions, our approach fuses the representations of the word complexity levels into the model to get better control of lexical complexity. And we demonstrate the feasibility of the approach for both training models from scratch and fine-tuning the pre-trained models. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Experimental results show that our approach provides more precise control over lexical complexity, as well as better fluency and diversity.