Papers with monolingual embedding spaces
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings (2021.acl-srw)
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| Challenge: | Unsupervised cross-lingual word embedding methods learn a linear transformation matrix that maps two monolingual embeddable spaces that are separately trained with monolingual corpora. |
| Approach: | They propose a method that maps two monolingual embedding spaces that are separately trained with monolingual corpora using a pseudo-parallel corpus. |
| Outcome: | The proposed method outperforms other methods given the same amount of data and shows that using a pseudo-parallel corpus makes the source and target corpora (partially) parallel . |
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 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. |
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces (2023.findings-emnlp)
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| Challenge: | Existing attempts to control relative isomorphism of different spaces fail to consider lexical variations of semantically similar words . Existing methods for building bilingual dictionaries rely on geometric similarity of individual spaces . |
| Approach: | They propose a method that incorporates the impact of lexical variations of semantically similar words into the training objective. |
| Outcome: | The proposed method outperforms existing research by improving the average P@1 by 63.6%. |