A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Word Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations
2017
International Joint Conference on Natural Language Processing
The research question we explore in this study is how to obtain syntactically plausible word representations without using human annotations. Our underlying hypothesis is that word ordering tests, or linearizations, is suitable for learning syntactic knowledge about words. To verify this hypothesis, we develop a differentiable model called Word Ordering Network (WON) that explicitly learns to recover correct word order while implicitly acquiring word embeddings representing syntactic knowledge.
dblp:conf/ijcnlp/NishidaN17
fatcat:minavrybifegxiy2hsk6w7iif4