Bilingual Lexicon Induction through Unsupervised Machine Translation

By Mikel Artetxe, Gorka Labaka, Eneko Agirre. 2019

60 citations recorded by the source corpus.

A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation. This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrase-table, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques. As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings. When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Cross-lingual Retrieval for Iterative Self-Supervised Training (2020, background): “Cross-Lingual Representations Various previous works have explored leveraging cross-lingual word representations to build parallel dictionaries and phrase tables, then applying them to downstream tasks [1, 2, 3, 4, 31, 36].”
On the Cross-lingual Transferability of Monolingual Representations (2019, neutral): “While previous work has questioned evaluation methods in this research area (Glavaš et al., 2019; Artetxe et al., 2019), our results provide evidence that existing methods are not competitive in challenging downstream tasks and that mapping between two fixed embedding spaces may be overly restrictiv”
A Survey of Cross-lingual Word Embedding Models (2017, background): “Recently, Artetxe, Labaka, and Agirre (2019) propose an alternative retrieval method that relies on building a phrase-based MT system from the cross-lingual word embeddings.”