  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.
