Papers by Yerai Doval
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
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| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
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. |
| Approach: | They propose to evaluate multiple cross-lingual word embedding models and compare their strengths and limitations to evaluate their effectiveness. |
| Outcome: | The proposed models perform well with noisy text and language pairs with major differences. |
Cross-Lingual Word Embeddings for Turkic Languages (2020.lrec-1)
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| Challenge: | Existing techniques to align monolingual embeddings are difficult to use because of low resources. |
| Approach: | They propose to use existing techniques to align monolingual embedding spaces for Turkic, Uzbek, Azeri, Kazakh and Kyrgyz languages. |
| Outcome: | The proposed techniques outperform existing techniques on bilingual dictionaries and an extrinsic task. |