Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport
Kelly Marchisio | Ali Saad-Eldin | Kevin Duh | Carey Priebe | Philipp Koehn
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
Kelly Marchisio | Youngser Park | Ali Saad-Eldin | Anton Alyakin | Kevin Duh | Carey Priebe | Philipp Koehn
Findings of the Association for Computational Linguistics: EMNLP 2021
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.
- Kelly Marchisio 2
- Kevin Duh 2
- Carey Priebe 2
- Philipp Koehn 2
- Youngser Park 1
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