Word embeddings are substantially successful in capturing semantic relations among words. However, these lexical semantics 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 for definition modeling, which can generate reasonable and understandable context-dependent definitions. Moreover, we introduce usage modeling and study whether it is possible to utilize embeddings to generate example sentences of words. These ways are a more direct and explicit expression of embedding’s semantics for better interpretability. We extend the single task model to multi-task setting and investigate several joint multi-task models to combine usage modeling and definition modeling together. Experimental results on existing Oxford dataset and a new collected Oxford-2019 dataset show that our single-task model achieves the state-of-the-art result in definition modeling and the multi-task learning methods are helpful for two tasks to improve the performance.

词嵌入在捕获词之间的语义关系方面非常成功。但是，这些词汇语义很难解释。定义建模提供了一种更直观的方式来评估嵌入，方法是利用它们生成对应单词的自然语言定义。此任务对于实际应用和深入理解单词表示形式具有重要意义。我们提出了一种新颖的定义建模框架，该框架可以生成合理且可理解的上下文相关定义。此外，我们介绍了用法建模并研究是否有可能利用嵌入来生成单词例句。这些方法是对嵌入语义的更直接和明确的表达，以实现更好的可解释性。我们将单任务模型扩展到多任务设置，并研究几个联合的多任务模型，以将使用模型和定义模型结合在一起。在现有牛津数据集和新收集的牛津2019数据集上的实验结果表明，我们的单任务模型在定义建模方面达到了最新的结果，多任务学习方法有助于两个任务提高性能。