| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Similar Papers
A Call for More Rigor in Unsupervised Cross-lingual Learning (2020.acl-main)
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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. |
| Approach: | They propose to define "multilingual learning" as learning a common model for two or more languages from raw text, without any downstream task labels. |
| Outcome: | The proposed model is based on a model with no parallel data and abundant monolingual data. |
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. |
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. |
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020.lrec-1)
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| Challenge: | Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping . |
| Approach: | They reproduce the experiments of Artetxe and Sgaard (2018) . they propose a robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. |
| Outcome: | The proposed method is feasible with minor assumptions, and it is able to be replicated in four languages. |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
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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. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
| Outcome: | The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks. |
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
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| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
| Approach: | They propose an unsupervised initialization method that exploits structural similarity of embeddings and a robust self-learning algorithm that iteratively improves it. |
| Outcome: | The proposed method achieves the best published results in standard datasets even surpassing previous supervised systems. |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings (P19-1)
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| Challenge: | Existing methods for cross-lingual word embeddings have limited results . existing methods require little or no cross-linguistic signal to work . |
| Approach: | They compare offline mapping methods to an extension of skip-gram that jointly learns both embedding spaces. |
| Outcome: | The proposed method yields more isomorphic embeddings, is less sensitive to hubness, and achieves stronger results in bilingual lexicon induction. |
Unsupervised Cross-lingual Transfer of Word Embedding Spaces (D18-1)
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| Challenge: | Existing methods for cross-lingual word mapping require cross-linguistic supervision, but this is not available for many low resource languages. |
| Approach: | They propose an unsupervised method that learns transformation functions over corresponding word embedding spaces using a distributed distributional matching algorithm. |
| Outcome: | The proposed method performs better on bilingual lexicon induction and cross-lingual word similarity prediction datasets than other supervised and unsupervised methods. |
On the Limitations of Unsupervised Bilingual Dictionary Induction (P18-1)
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| Challenge: | Unsupervised machine translation does not require cross-lingual supervision, whether a dictionary, translations, or comparable corpora. |
| Approach: | They propose an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction that exploits a weak supervision signal from identical words. |
| Outcome: | The proposed model relies heavily on an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |