Lexical Complexity Controlled Sentence Generation for Language Learning (2023)

Abstract: “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.”

## introduction in the fields of language teaching and acquisition, language instructors and textbook compilers need to make teaching materials with example sentences, either synthetically designed or from authentic resources @xcite . in most cases, they are required to create appropriate example sentences that only use the words at particular complexity for language learners passing through different learning levels @xcite , which is very time-consuming and exhausting. automatically generating good examples can support educators and language learners in obtaining, analyzing, and selecting proper example sentences. besides, it can also assist in the development of graded reading materials @xcite @xcite . for language learners, good examples are not only required to be fluent and diverse but also match the level of the learners, especially the level of vocabulary. therefore, it is necessary to effectively control the lexical complexity in good examples generation, which is a task of controllable text generation. controllable text generation (ctg), a significant area of natural language generation, contains a series of tasks that aim to generate text according to the given controlled requirements @xcite . ctg systems usually focus on controlling text attributions such as sentiment @xcite @xcite , topic @xcite @xcite or keywords @xcite @xcite , generating poems or couplets with specific formats @xcite al., level a and b: level a and c: level a level b level c the water … light peach ... palm exposure … tree need the tree needs water. this peach tree needs light. palm trees need full sun exposure. ## experiments our experiments are based on the two datasets introduced in section 3. besides the strong baselines of controlled decoding, prompting and reranking mentioned in section 4.2, we generate the sentence by setting the keys as the input directly as the basic baseline (k2s). this baseline does not require complexity levels, which are just learnt from the data. our evaluations include automatic evaluation and human evaluation. the automatic metrics have been introduced in the section 3. ## related work lexical constraint text generation is to generate a complete text sequence, given a set of keywords as constraints @xcite . previous works involve enhanced beam search @xcite and the stochastic search methods @xcite . currently, seq2seq-based models such as transformer and pre-trained models have been increased in generation with lexical constraint @xcite @xcite @xcite . but lexically constrained text generation is not able to control the complexity of words used in the generation, which is different from our work. text readability assess research has shown that lexical complexity is also a crucial aspect of evaluating the complexity of a text for text readability assess task @xcite . in the relevant study of sentence-level readability, it is generally accepted that apart from sentence length, the most predictive indicator is the number of difficult words in the sentence @xcite . in our work, we follow the definition and vocabulary of lexical complexity of text readability assess. text simplification in text simplification field, lexical substitution, the replacement of complex words with simpler alternatives, is an integral part of sentence simplification and has been the subject of previous work @xcite . differently, our work can strictly control the lexical complexity levels of the generated sentence, not only simplify the lexical complexity. ## conclusions to summarize, we introduce a new task of lexical complexity controlled sentence generation, where word complexity must be strictly controlled in generating. to promote the development of this task, we develop two datasets and four metrics for the controlled element. in this paper, we also develop a series of alternate solutions for this task and propose a novel method based on complexity embedding to obtain better control of lexical complexity in a generation. our results indicate that the complexity embedding method has better performance in controlling the lexical complexity and competitive generation quality. dataset train valid test total english 180,000 16,000 3,615 199,615 chinese 140,000 14,000 2,661 156,661