| 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. |
| Outcome: | The proposed model improves on WAT17 English-Japanese and IWSLT14 German-English translation tasks without increasing the number of parameters. |
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| Challenge: | Recent studies on multilingual representations focus on whether there is an emergence of language-independent representations or whether multilingual models partition their weights among different languages. |
| Approach: | They analyze encoder self-attention and encoder-decoder attention heads in a multilingual neural translation model. |
| Outcome: | The proposed model is based on a multilingual neural translation model with a language-independent representation. |
Attention over Heads: A Multi-Hop Attention for Neural Machine Translation (P19-2)
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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. |
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Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies show that the attention heads in Transformer are not equal. |
| Approach: | They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck . |
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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. |
| Outcome: | The proposed model reduces BLEU scores by adding a single learned cross attention head to an otherwise hard-coded Transformer. |
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 . |
| Approach: | They propose to replace all but one attention head of each encoder layer with simple fixed – non-learnable – attentive patterns that are solely based on position and do not require external knowledge. |
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Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (P19-1)
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| Challenge: | et al., 2017) show that multi-head attention is important for neural machine translation. |
| Approach: | They evaluate the contribution made by individual attention heads to the overall performance of the Transformer model and analyze the roles played by them in the encoder. |
| Outcome: | The proposed pruning method removes the vast majority of heads without affecting performance. |
Character-Level Translation with Self-attention (2020.acl-main)
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| Challenge: | Existing models for character-level neural machine translation operate on word-level, which makes them memory inefficient because of large vocabulary sizes. |
| Approach: | They propose a transformer-based model and a novel variant that uses convolutions to combine information from nearby characters to facilitate character interactions. |
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Enhancing Machine Translation with Dependency-Aware Self-Attention (2020.acl-main)
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| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
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| Outcome: | The proposed approach improves translation quality on English-German and English-Turkish translation tasks and in low-resource scenarios. |
Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation (2022.naacl-srw)
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| Challenge: | Existing approaches to address speech tasks with a self-attention mechanism are expensive and lead to information loss. |
| Approach: | They propose a Transformer-based model which uses different attention mechanisms on each head to bias the self-attention towards the extraction of more diverse token interactions. |
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Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)
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| Challenge: | Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information. |
| Approach: | They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms. |
| Outcome: | The proposed approach improves on English-to-German and NIST Chinese-to English translation tasks. |