Papers with denoising objective
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining (2022.naacl-main)
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| Challenge: | Existing methods for multilingual sequence-to-sequence pretraining rely on monolingual corpora and do not use strong cross-lingual signal contained in parallel data. |
| Approach: | They propose a method that replaces monolingual words with a bilingual dictionary and predicts the reference translation according to a parallel corpus instead of recovering the original sequence. |
| Outcome: | The proposed method improves machine translation and cross-lingual natural language inference by 2.0 BLEU points and 6.7 accuracy points over existing methods at a fraction of their computational cost. |
Learning Better Masking for Better Language Model Pre-training (2023.acl-long)
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| Challenge: | Existing PrLMs adopt a Random-Token Masking strategy with a fixed masking ratio and different contents are masked by an equal probability throughout the training. |
| Approach: | They propose two scheduled masking approaches that adaptively tune masking ratio and masked content in different training stages, which improves pre-training efficiency and effectiveness. |
| Outcome: | The proposed methods improve the pre-training efficiency and effectiveness on the downstream tasks. |
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (2021.emnlp-main)
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| Challenge: | Multilingual unsupervised machine translation is a computationally expensive and hard to tune approach . auxiliary parallel data is used to train translation systems from monolingual data . |
| Approach: | They propose to use auxiliary parallel language pairs to train unsupervised machine translations . they propose to add auxiliary languages to pre-trained mBART-50 models with denoising adapters . |
| Outcome: | The proposed approach is on-par with back-translation and allows adding unseen languages incrementally. |
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding (2024.findings-emnlp)
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Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
| Challenge: | End-to-end models for Spoken Language Understanding have been autoregressive, resulting in higher latencies. |
| Approach: | They propose a method that uses Connectionist Temporal Classification to train robust non-autoregressive deliberation models. |
| Outcome: | The proposed method achieves 10x latency reduction over autoregressive models while preserving ability to correct ASR mistranscriptions. |