Non-autoregressive Streaming Transformer for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Simultaneous machine translation models are trained to strike a balance between latency and translation quality. |
| Approach: | They propose a non-autoregressive streaming Transformer which generates blank tokens and decodes repetitive tokens to adjust its READ/WRITE strategy flexibly. |
| Outcome: | The proposed model outperforms previous strong autoregressive models on various benchmarks on siMT. |
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| Challenge: | Existing methods for siMT focus on the Encoder-Decoder architecture, but there are limitations in training and inference. |
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| Challenge: | Existing non-autoregressive neural machine translation models are slow to learn the dependency between output tokens. |
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| Challenge: | Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles. |
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| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
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| Challenge: | Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners. |
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| Challenge: | Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts. |
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