“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 controlledsentence generation, which requires precise control over the lexical complexity in thekeywords to examples generation and better fluency and semantic consistency. The chal-lenge of this task is to generate fluent sentences only using words of given complexitylevels. We propose a simple but effective approach for this task based on complexityembedding while controlling sentence length and syntactic complexity at the decodingstage. Compared with potential solutions, our approach fuses the representations of theword complexity levels into the model to get better control of lexical complexity. Andwe demonstrate the feasibility of the approach for both training models from scratch andfine-tuning the pre-trained models. To facilitate the research, we develop two datasetsin English and Chinese respectively, on which extensive experiments are conducted. Ex-perimental results show that our approach provides more precise control over lexicalcomplexity, as well as better fluency and diversity.”

“The definition generation task aims to generate a word’s definition within a specific context automatically. However, owing to the lack of datasets for different complexities, the definitions produced by models tend to keep the same complexity level. This paper proposes a novel task of generating definitions for a word with controllable complexity levels. Correspondingly, we introduce COMPILING, a dataset given detailed information about Chinese definitions, and each definition is labeled with its complexity levels. The COMPILING dataset includes 74,303 words and 106,882 definitions. To the best of our knowledge, it is the largest dataset of the Chinese definition generation task. We select various representative generation methods as baselines for this task and conduct evaluations, which illustrates that our dataset plays an outstanding role in assisting models in generating different complexity-level definitions. We believe that the COMPILING dataset will benefit further research in complexity controllable definition generation.”