论文标题
动态上下文化的单词嵌入
Dynamic Contextualized Word Embeddings
论文作者
论文摘要
用单个向量表示单词的静态词嵌入无法捕获不同语言和外语言环境中单词含义的变异性。在上下文化和动态单词嵌入的先前工作的基础上，我们介绍了动态上下文化的单词嵌入，这些单词嵌入将单词表示为语言和外语言上下文的函数。基于预审前的语言模型（PLM），共同的动态上下文嵌入模型时间和社交空间，这使它们对于涉及语义可变性的一系列NLP任务有吸引力。我们通过四个英语数据集的定性和定量分析来强调潜在的应用程序方案。
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.
