Papers with architecture designs
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
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Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |
Learned Adapters Are Better Than Manually Designed Adapters (2023.findings-acl)
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| Challenge: | Existing approaches to improve adapter-based tuning are sub-optimal . a learning framework is proposed to learn the optimal adapter architectures . |
| Approach: | They propose a framework to automatically learn optimal adapter architectures for better task adaptation of pre-trained models. |
| Outcome: | The proposed framework outperforms the previous parameter-efficient tuning baselines while tuning comparable or fewer parameters. |
DGST: a Dual-Generator Network for Text Style Transfer (2020.emnlp-main)
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| Challenge: | Existing studies on text style transfer focus on altering sentiment words to preserve attribute-independent information. |
| Approach: | They propose a Dual-Generator network architecture for text Style Transfer using two generators. |
| Outcome: | The proposed model performs better than existing models on Yelp and IMDb datasets. |