Incorporating Sememes into Chinese Definition Modeling

Abstract

Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we built two novel datasets based on Chinese Concept Dictionary (CCD) and Chinese WordNet (CWN) respectively. Each dataset contains triples of a word, sememes, and a 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 both datasets demonstrate that by incorporating sememes, our model can generate definitions with more concrete information. And the best model that we proposed outperforms the state-of-the-art method by a large margin on both datasets.