| Challenge: | Existing efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation have focused on assessing the encoded representations or interpreting the multi-head self-attentions. |
| Approach: | They propose to use Transformer-based encoder-decoder architectures to analyze how information is propagated through each module of each decoder layer. |
| Outcome: | The proposed model can be dropped with minimal loss of performance on three translation datasets and can be used to train and inference faster. |
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| Challenge: | Existing interpretability methods have been proposed to interpret the inner workings of Transformer models at different levels of precision and complexity. |
| Approach: | They propose a method to analyze encoder-decoder Transformers by using the decoder module Model Output encoder to cross-attend representations of intermediate encoder activations instead of using the default output. |
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Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer (2022.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) relies on source sentence and target prefix attributions for each input token. |
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Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers (2021.naacl-main)
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| Challenge: | Neural Machine Translation (NMT) has attracted wide attention in recent years. |
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Exploiting Deep Representations for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers. |
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Rethinking the Value of Transformer Components (2020.coling-main)
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| Challenge: | Empirical results show that certain components are more important than others . we propose a new training strategy that can improve Transformer models by distinguishing unimportant components . |
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What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks. |
| Approach: | They propose to deepen the decoder layer in a Transformer model to reduce the difficulty of deep learning. |
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Is Encoder-Decoder Redundant for Neural Machine Translation? (2022.aacl-main)
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| Challenge: | Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. |
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Learning Deep Transformer Models for Machine Translation (P19-1)
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Revisiting Robust Neural Machine Translation: A Transformer Case Study (2021.findings-emnlp)
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| Challenge: | Recent advances in NMT have shown promising results but are vulnerable to noise. |
| Approach: | They propose a data-driven technique called Target Augmented Fine-tuning to incorporate noise during training. |
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Syntactically Supervised Transformers for Faster Neural Machine Translation (P19-1)
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| Challenge: | Standard decoders for neural machine translation generate a single token per timestep, which slows inference . a series of controlled experiments demonstrates that SynST decodes sentences 5x faster than the baseline autoregressive Transformer. |
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