Multilingual Models for Compositional Distributed Semantics

TL;DR

This paper introduces a new multilingual semantic embedding technique that aligns sentence and document representations across languages without relying on word alignments, improving cross-lingual classification performance.

Contribution

It presents a novel approach for learning multilingual semantic representations using joint-space embeddings without syntactic information or word alignments.

Findings

Outperforms previous state-of-the-art in cross-lingual document classification

Embeddings capture semantic relationships across languages without parallel data

Models successfully extend from sentence to document level representations

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.