Despite the success achieved on various natural language processing tasks,
word embeddings are difficult to interpret due to the dense vector
representations. This paper focuses on interpreting the embeddings for various
aspects, including sense separation in the vector dimensions and definition
generation. Specifically, given a context together with a target word, our
algorithm first projects the target word embedding to a high-dimensional sparse
vector and picks the specific dimensions that can best explain the semantic
meaning of the target word by the encoded contextual information, where the
sense of the target word can be indirectly inferred. Finally, our algorithm
applies an RNN to generate the textual definition of the target word in the
human readable form, which enables direct interpretation of the corresponding
word embedding. This paper also introduces a large and high-quality
context-definition dataset that consists of sense definitions together with
multiple example sentences per polysemous word, which is a valuable resource
for definition modeling and word sense disambiguation. The conducted
experiments show the superior performance in BLEU score and the human
evaluation test.

本文研究了词向量的解释问题，提出了一种算法可通过上下文信息和目标词语推断其意义，进而利用循环神经网络生成该词汇的定义，实现对词向量的直接解释。同时，作者提出了一个高质量的词汇上下文 - 定义数据集，用于词义消歧与定义建模。经实验测试，该方法在 BLEU 评分和人工评估测试中表现出卓越的性能。