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.

该研究提出了一项新的任务来生成控制复杂度级别的单词定义，并介绍了一个包含详细信息的数据集来帮助模型生成不同复杂度级别的定义，并选择了一些代表性的方法作为基线并进行评估，表明我们的数据集对于生成不同复杂度级别的定义有巨大帮助，这将有助于进一步研究复杂度可控的定义生成。

COMPILING: 为中文可控复杂度定义生成编写的基准数据集

COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation

Natural Language Generation (NLG) for task-oriented dialogue systems focuses
on communicating specific content accurately, fluently, and coherently. While
these attributes are crucial for a successful dialogue, it is also desirable to
simultaneously accomplish specific stylistic goals, such as response length,
point-of-view, descriptiveness, sentiment, formality, and empathy. In this
work, we focus on stylistic control and evaluation for schema-guided NLG, with
joint goals of achieving both semantic and stylistic control. We experiment in
detail with various controlled generation methods for large pretrained language
models: specifically, conditional training, guided fine-tuning, and guided
decoding. We discuss their advantages and limitations, and evaluate them with a
broad range of automatic and human evaluation metrics. Our results show that
while high style accuracy and semantic correctness are easier to achieve for
more lexically-defined styles with conditional training, stylistic control is
also achievable for more semantically complex styles using discriminator-based
guided decoding methods. The results also suggest that methods that are more
scalable (with less hyper-parameters tuning) and that disentangle content
generation and stylistic variations are more effective at achieving semantic
correctness and style accuracy.

本研究着重于架构引导的自然语言生成中的风格控制和评估，旨在同时实现语义和风格控制，通过条件训练、引导微调和引导解码等各种控制生成方法的实验来评估其优缺点，并用广泛的自动和人工评估指标来评估，结果表明，使用判别器引导解码更适合处理风格较为语义复杂的任务，同时这种方法也更加可扩展，效果更好。