Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition. We present two novel models to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates sememes for generating the definition with an adaptive attention mechanism. It has the capability to decide which sememes to focus on and when to pay attention to sememes. SAAM further replaces recurrent connections in AAM with self-attention and relies entirely on the attention mechanism, reducing the path length between word, sememes and definition. Experiments on CDM demonstrate that by incorporating sememes, our best proposed model can outperform the state-of-the-art method by +6.0 BLEU.

本文介绍了一个用于生成中文词汇释义的语料库（CDM），并且通过引入Adaptive-Attention模型和Self- and Adaptive-Attention模型等新模型，成功提升了中文释义生成的精度和效果。实验结果表明，该模型比现有的最先进模型的BLEU指标要高出6.0个百分点。

将义元加入汉语定义建模