Why is unsupervised alignment of English embeddings from different algorithms so hard? (D18-1)
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| Challenge: | a new paper challenges word embedding algorithms to align independent English word embeds with 100% precision . authors show that when two different embeddables are used, they fail to do so . |
| Approach: | They propose to use unsupervised bilingual dictionary induction to study English-English alignments. |
| Outcome: | The proposed approach is more of a challenge than a technical contribution . it shows that the results challenge unsupervised bilingual dictionary induction algorithms . |
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
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| Challenge: | Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings without supervision, but their performance for distant languages is still not satisfactory. |
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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. |
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| Challenge: | Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. |
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
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