A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping (2020.lrec-1)
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| Challenge: | Existing methods for word embeddings are limited to a single, unannotated corpus, which means that word representations with similar meaning in distinct languages can be very different. |
| Approach: | They propose an unsupervised method for cross-lingual word embedding mapping that uses stochastic initialization and isometric initialization to verify the method's robustness. |
| Outcome: | The proposed method is robust on different embedding representations and new language pairs, particularly those involving Slavic languages like Polish or Czech. |
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| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
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| Challenge: | Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping . |
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| Challenge: | Existing research on unsupervised cross-lingual learning has focused on purely unsupervised learning without any parallel data for most of the world's languages. |
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On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning (2020.lrec-1)
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| Challenge: | Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. |
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
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| Challenge: | Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embeddable spaces are structurally similar. |
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