| Challenge: | Existing methods for unsupervised bilingual word embeddings are limited by the dissimilarity between the word embedded spaces. |
| Approach: | They propose a method that trains unsupervised bilingual word embeddings jointly on parallel data generated through unsupervised machine translation. |
| Outcome: | The proposed method outperforms unsupervised mapped bilingual word embeddings in cross-lingual NLP tasks. |
Similar Papers
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. |
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs (N19-1)
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| Challenge: | Recent research has found that a shared bilingual word embedding space can be induced by projecting monolingual word embeds from two languages without any bilingual supervision. |
| Approach: | They propose a framework for learning unsupervised multilingual word embeddings that mitigates instability issues for distant language pairs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two downstream tasks outperforming even supervised baselines. |
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. |
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation (P19-1)
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| Challenge: | Unsupervised bilingual word embedding (UBWE) has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. |
| Approach: | They propose two methods that train UNMT with UBWE agreement . they propose to use UBwe to initialize word embedding in UNMT . |
| Outcome: | The proposed methods outperform conventional methods on several language pairs. |
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 Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
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| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| 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. |
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (N19-1)
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| Challenge: | Unsupervised BWE methods are evaluated on word translation or word similarity tasks. |
| Approach: | They propose a method that learns sentiment-specific word representations for two languages in a common space without cross-lingual supervision. |
| Outcome: | The proposed method outperforms previous unsupervised BWE methods and even supervised Bwe methods on three language pairs for cross-lingual sentiment analysis. |
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. |