Dimensionality reduction

Gene

Transformation (genetics)

Algorithm

Normalization (sociology)

Pure mathematics

Curse of dimensionality

Weighting

Inverse

Biochemistry

Computer science

Lexicon

Anthropology

Embedding

Radiology

Natural language processing

Word embedding

Mathematics

Orthogonal transformation

Sociology

Artificial intelligence

Geometry

Medicine

Chemistry

Linear map

Word (group theory)

Proceedings of the ... AAAI Conference on Artificial Intelligence

Using a dictionary to map independently trained word embeddings to a shared space has shown to be an effective approach to learn bilingual word embeddings. In this work, we propose a multi-step framework of linear transformations that generalizes a substantial body of previous work. The core step of the framework is an orthogonal transformation, and existing methods can be explained in terms of the additional normalization, whitening, re-weighting, de-whitening and dimensionality reduction steps. This allows us to gain new insights into the behavior of existing methods, including the effectiveness of inverse regression, and design a novel variant that obtains the best published results in zero-shot bilingual lexicon extraction. The corresponding software is released as an open source project.

Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations