Мета-векторные представления

Word embeddings – distributed represen- tations of words – in deep learning are beneficial for many tasks in NLP. How- ever, different embedding sets vary greatly in quality and characteristics …...

AbstractDistributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those …...

AbstractSemantic models derived from visual information have helped to overcome some of the limitations of solely text-based distributional semantic models. Researchers have demonstrated that text and image-based representations encode complementary …...

AbstractWe address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word …...