Papers with convolutional layers
JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT (2022.emnlp-demos)
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| Challenge: | JoeyS2T is a simple, lightweight NMT extension for speech-to-text tasks such as automatic speech recognition and end-to end speech translation. |
| Approach: | JoeyS2T is a JoeyNMT extension for automatic speech recognition and end-to-end speech translation. |
| Outcome: | JoeyS2T performs competitively on English speech recognition and English-to-German speech translation benchmarks. |
An Improved Model for Voicing Silent Speech (2021.acl-short)
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| Challenge: | Existing models for voicing silent speech use hand-designed features instead of EMG signals. |
| Approach: | They propose to use facial electromyography signals as input instead of hand-designed features to give the model greater flexibility to learn its own features. |
| Outcome: | The proposed model improves state-of-the-art on an open vocabulary intelligibility evaluation by 25.8%. |
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (P19-1)
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| Challenge: | Multi-passage reading comprehension requires the ability to combine cross-passages information and reason over multiple passages to infer the answer. |
| Approach: | They propose a Dynamic Self-attention Network (DynSAN) which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets and further ablation validates the effectiveness of its components. |
Aspect Based Sentiment Analysis with Gated Convolutional Networks (P18-1)
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| Challenge: | Aspect-based sentiment analysis can provide more detailed information than general sentiment analysis. |
| Approach: | They propose a model based on convolutional neural networks and gating mechanisms which can selectively output the sentiment features according to the given aspect or entity. |
| Outcome: | The proposed model can selectively output sentiment features according to the given aspect or entity. |
Double Path Networks for Sequence to Sequence Learning (C18-1)
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| Challenge: | Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular . |
| Approach: | They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning. |
| Outcome: | The proposed method significantly improves performance over state-of-the-art systems. |