How to develop a translation model that can take both speech and text as input and translate to target language? Can we borrow inspiration from human brain study to improve the speech translation models?
Reading Time: About 15 minutes.
About 10 min
- Multilingual MT5
- Pre-training5
- BERT4
- Random Aligned Substitution2
- Zero-shot Translation2
- mRASP2
- Speech Translation2
- MT Evaluation2
- Transformer2
- Knowledge Assessment1
- Risk Ratio1
- Vocabulary Learning1
- Optimal Transport1
- Catastrophic Forgetting1
- Model Protection1
- Relation Extraction1
- Fact Verafication1
- Reasoning1
- Logic-regularized neural network1
- Embedding1
- Shared Semantic Memory1
- Chimera1
- Language Modelling1
- Imagination1
- Visual Machine Translation1
- ImagiT1
- Model Capacity1
- Language-specific Sub-network1
- GPU Acceleration1
- CUDA1
- Variational Inference1
- Latent Variable Model1
- Semi-supervised Learning1
- Contrastive Learning1
- BERTScore1
- COMET1
- Translation Memory1
- Recurrent Attention1
- Self-training1
- Unsupervised Machine Translation1