An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces

TL;DR

This paper compares Euclidean and graph-based methods for bilingual lexicon induction from word embeddings, revealing their complementary strengths and introducing a combined approach for improved performance.

Contribution

It provides the first comparison of Euclidean and graph-based BLI methods and demonstrates their complementary nature when integrated.

Findings

Graph-based methods outperform in low-resource scenarios.

Combined approaches improve lexicon induction accuracy.

Euclidean and graph methods complement each other.

Abstract

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node's graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://github.com/kellymarchisio/euc-v-graph-bli.