Year: 2014
Type: conference-paper
Abstract: We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings.Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences.The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages.We extend our approach to learn semantic representations at the document level, too.We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art.Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.
DOI: 10.3115/v1/p14-1006
Authors: Karl Moritz Hermann, Phil Blunsom
Institution: University of Oxford
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2142074148