Papers with denoising autoencoding
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
| Approach: | They exploit linguistic overlap to facilitate translation to and from low-resource languages . they use monolingual data and parallel data in related high-resourced languages based on their method . |
| Outcome: | The proposed method significantly improves translation into low-resource language compared to baselines on 7 languages from three different language families. |
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders (P19-1)
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| Challenge: | Existing approaches to train multiple languages with a shared encoder and multiple decoders are based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. |
| Approach: | They propose a multilingual unsupervised NMT scheme which trains multiple languages with a shared encoder and multiple decoders. |
| Outcome: | The proposed model performs better than the separately trained bilingual models on monolingual corpora and improves by 1.48 BLEU points on WMT test sets. |
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale (2024.naacl-long)
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| Challenge: | Multilingual machine translation (MMT) is a key tool for improving translation in low-resource languages. |
| Approach: | They examine how denoising autoencoding and backtranslation impact multilingual machine translation under different data conditions and model scales. |
| Outcome: | The proposed method improves translation efficiency in low-resource languages by using denoising autoencoding (DAE) and backtranslation (BT) . |