Before revision:  However, the lexical semantics captured by these embeddings are difficult to interpret due to the dense vector representations. In order to improve the interpretability of word vectors, we explore definition modeling task and propose a novel framework (Semantics-Generator) to generate more reasonable and understandable context-dependent definitions.
After revision:  However, the lexical semantics captured by these embeddings are difficult to be interpreted. Definition modeling provides a more intuitive way to evaluate embeddings by utilizing them to generate natural language definitions of corresponding words. This task is of great significance for practical application and in-depth understanding of word representations. We propose a novel framework (Semantics-Generator) to generate more reasonable and understandable context-dependent definitions.