Papers with cross-lingual word embedding
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder (D18-1)
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| Challenge: | Unsupervised learning of cross-lingual word embeddings has fundamental limitations in translating sentences. |
| Approach: | They propose a method to improve word-by-word translation of cross-lingual embeddings using monolingual corpora without any back-translation. |
| Outcome: | The proposed system surpasses state-of-the-art unsupervised translation systems without costly iterative training. |
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies (P19-1)
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| Challenge: | Existing approaches to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies are limited to cognate languages. |
| Approach: | They propose to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies by using cross-lingual word embedding and injecting artificial noises. |
| Outcome: | The proposed methods outperform multilingual joint training by a large margin in five low-resource translation tasks. |
Density Matching for Bilingual Word Embedding (N19-1)
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| Challenge: | Recent approaches to cross-lingual word embeddings have been based on linear transformations between the embeddable vectors in the two languages. |
| Approach: | They propose a method that expresses two monolingual embedding spaces as probability densities and matches them using a Gaussian mixture model. |
| Outcome: | The proposed method can achieve competitive or superior performance on bilingual lexicon induction and cross-lingual word similarity data. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? (D19-1)
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| Challenge: | a series of bilingual lexicon induction experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods fail for a large number of language pairs. |
| Approach: | They propose to use fully unsupervised approaches to project monolingual embeddings into a shared cross-lingual space without any cross-linguistic signal. |
| Outcome: | The proposed methods fail for a large number of language pairs, but never surpass weakly supervised methods. |