MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)
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| Challenge: | Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes. |
| Approach: | They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset. |
| Outcome: | The proposed framework improves on four datasets and four BERT-like backbone models. |
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| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
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| Challenge: | Spoken language understanding (SLU) suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. |
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| Challenge: | Despite efforts to improve ASR robustness, errors from pipeline approaches can lead to error propagation. |
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| Challenge: | Large language models excel in speech processing tasks but their reliance on written text limits their application in real-world scenarios. |
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| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
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CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation (2023.emnlp-main)
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| Challenge: | Cascaded approach is the most popular choice for speech translation, but lacks robustness when dealing with noisy inputs. |
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Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition (2024.lrec-main)
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Yash Jain, David M. Chan, Pranav Dheram, Aparna Khare, Olabanji Shonibare, Venkatesh Ravichandran, Shalini Ghosh
| Challenge: | Existing methods for pre-training for automatic speech recognition (ASR) focus on single-stage pre-train followed by fine-tuning on downstream task. |
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Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
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GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)
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| Challenge: | Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages. |
| Approach: | They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance . |
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