| Challenge: | Recent proposed approaches to perform unsupervised language adaptation lack annotated resources in less-resourced languages. |
| Approach: | They propose to use Adversarial Training, Sentence Encoder Alignment and Shared-Private Architecture to perform unsupervised language adaptation without using aligned sentences. |
| Outcome: | The proposed approaches are more suitable when the source and target language datasets contain other variations in content besides the language shift. |
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| Challenge: | Recent work has shown superior performance for non-adversarial methods in more challenging language pairs. |
| Approach: | They propose to use adversarial autoencoder to map monolingual embeddings to a shared space and to put the target encoders as an adversary against the corresponding discriminator. |
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Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
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Simple and Effective Unsupervised Speech Translation (2023.acl-long)
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
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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. |
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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 . |
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Non-Adversarial Unsupervised Word Translation (D18-1)
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| Challenge: | Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. |
| Approach: | They propose a method that aligns two words in two languages and iteratively refines the alignment. |
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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. |
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Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models (2020.emnlp-main)
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| Challenge: | Recent work has shown the importance of training contextualised word embedding models on the domain of the target task of interest. |
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Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
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Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis (D19-1)
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| Challenge: | a new study examines the use of labeled and unlabeled corpora in political science research . large corporata often contain documents of a certain subject or type, but they are often unlabed . a recent study found that labeles with pertinent documents stem from a single source . |
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