| Challenge: | a new co-attentional neural structure is proposed for machine translation tasks . a higher-level and more abstract paradigm generalized from CCNs is proposed . |
| Approach: | They propose a paradigm that consists of two symmetric encoder modules and one decoder module connected with co-attention. |
| Outcome: | The proposed model outperforms the current Transformer model on translation tasks but the epoch time increases by circa 75%. |
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| Challenge: | Existing multihop attentions for machine comprehension are recurrent and hierarchical . a proposed multi-hop attention for the Transformer refines the attention for an output symbol many times . |
| Approach: | They propose a multi-hop attention for the Transformer which integrates attentions from each head. |
| Outcome: | The proposed model outperforms the baseline Transformer in terms of translation accuracy and speed. |
Mixed Multi-Head Self-Attention for Neural Machine Translation (D19-56)
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| Challenge: | Recent advances in neural machine translation have been made in the field of multi-head self-attention and there is no explicit mechanism to ensure that different attention heads capture different features. |
| Approach: | They propose a novel multi-head self-attention model which models not only global and local attention but also forward and backward attention in different attention heads. |
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Hard-Coded Gaussian Attention for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work has questioned the importance of multi-headed attention in achieving high translation quality. |
| Approach: | They develop a “hard-coded” attention variant without any learned parameters. |
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Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)
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Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation (2020.findings-emnlp)
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| Challenge: | Recent studies have shown that attention heads learn simple positional patterns . |
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Synchronous Syntactic Attention for Transformer Neural Machine Translation (2021.acl-srw)
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Learning Hard Retrieval Decoder Attention for Transformers (2021.findings-emnlp)
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| Challenge: | In this paper, we show that learning a hard retrieval attention that attends to a single token in a sentence is 1.43 times faster than the standard scaled dot-product attention. |
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Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks (2021.acl-long)
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| Challenge: | Prior research has found that only a few attention heads are important in each mono-lingual NLP task and pruning the remaining heads leads to comparable or improved performance of the model. |
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Recurrent Attention for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent research questions the importance of dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. |
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Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2021.emnlp-main)
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| Challenge: | a series of experiments show that fine-tuning only the cross-attention parameters is nearly as effective as fine-timing all parameters. |
| Approach: | They conduct experiments to fine-tune a translation model on data where either the source or target language has changed. |
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