Multilingual Models for Compositional Distributed Semantics
This work addresses the challenge of semantic understanding across diverse languages for applications like cross-lingual classification, though it appears incremental as it builds on existing distributional and embedding methods.
The authors tackled the problem of learning semantic representations across multiple languages by extending the distributional hypothesis to multilingual data and joint-space embeddings, resulting in models that outperformed prior state-of-the-art on cross-lingual document classification tasks.
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.