Papers with DAE
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm (D19-55)
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| Challenge: | Existing spell checkers for Chinese are based on denoising autoencoder and decoder paradigms that require a small amount of data to be effective. |
| Approach: | They propose a Chinese spell checker based on a new paradigm which consists of a denoising autoencoder and a decoder. |
| Outcome: | The proposed spell checker is faster, more Adaptable to simplified and traditional Chinese texts and has a much simpler structure to be as much Powerful in error detection and correction. |
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models (2023.emnlp-main)
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| Challenge: | Recent advances in NLP have led to the use of pre-trained Transformer models for transfer learning tasks becoming the most common way to solve target tasks. |
| Approach: | They propose a 3-phase technique to adjust a base model for a classification task by adapting the model’s signal to the data distribution and a new data augmentation approach for Supervised Contrastive Learning to correct the unbalanced datasets. |
| Outcome: | The proposed method is compared with other methods and compares it with other approaches. |
Modularized Multilingual NMT with Fine-grained Interlingua (2024.naacl-long)
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| Challenge: | Neural Machine Translation (MNMT) systems lack layer-sharing to generate interlingua features . however, layer-share structure does not guarantee explicit propagation of language-specific features to respective decoders. |
| Approach: | They propose to share top of language-specific encoder layers to enable interlingua features . their method demonstrates an improved average BLEU score by "+2.90" in En-to-Any directions . |
| Outcome: | The proposed approach improves the BLEU score by "+2.90" in En-to-Any directions and by "+1.06" in zero-shot translation. |
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) . |