In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.
翻译:在这项工作中,我们提出了一种有效的方法,用于语言矢量表达的语义专业化。为此,我们使用传统的词嵌入和专门化方法,更好地捕捉言词之间的关系。在我们的方法中,我们利用巴比尔网等丰富的词汇资源提供的外部知识。我们还表明,我们提议的基于对抗性神经网络的专门化后方法与瓦塞尔斯坦距离可以改进两种任务的最新方法:词相似和对话状态跟踪。