| Challenge: | Existing models of self-attention networks lack the ability to capture dependencies regardless of distance and can be enhanced with multi-head attention. |
| Approach: | They propose a convolutional self-attention network which can be enhanced by multi-head attention by allowing the model to attend to information from different representation subspaces. |
| Outcome: | The proposed model outperforms existing models on improving locality of SANs on different language pairs and model settings. |
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| Challenge: | SANs are an integral part of successful neural networks such as Transformer . training SAN on a task or pretraining them on language modeling requires large amounts of data and compute resources. |
| Approach: | They propose to modify SANs to enable faster learning, i.e., higher accuracies after fewer update steps. |
| Outcome: | The proposed modifications enable faster learning, i.e., higher accuracies after fewer update steps. |
Self-Attention with Structural Position Representations (D19-1)
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| Challenge: | Experimental results show that SANs can't encode positions of input words . SAN's are currently lacking in encoding positions of words based on position-unaware "bagof-words" theory . |
| Approach: | They propose to augment SANs with structural position representations to capture latent structure of input sentence. |
| Outcome: | The proposed approach consistently outperforms the sequential representations on translation tasks. |
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. |
| Outcome: | The proposed model improves on WAT17 English-Japanese and IWSLT14 German-English translation tasks without increasing the number of parameters. |
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures (D18-1)
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| Challenge: | Recent studies show that non-recurrent architectures outperform RNNs in neural machine translation. |
| Approach: | They hypothesize that CNNs and self-attentional networks could extract semantic features from source text. |
| Outcome: | The proposed architectures outperform RNNs on two tasks: subject-verb agreement and word sense disambiguation. |
Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)
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| Challenge: | Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information. |
| Approach: | They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts . |
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How Does Selective Mechanism Improve Self-Attention Networks? (2020.acl-main)
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| Challenge: | Experimental results show that selective SANs outperform the standard SAN by paying more attention to content words that contribute to the meaning of the sentence. |
| Approach: | They propose to implement selective SANs with a flexible Gumbel-Softmax to improve word order encoding and structure modeling. |
| Outcome: | The proposed system outperforms the standard SANs on several representative NLP tasks including natural language inference, semantic role labelling, and machine translation. |
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. |
| Approach: | They propose a parameter-free, dependency-aware self-attention mechanism that integrates syntactic knowledge into a Transformer model and propose 'a parameter free approach' they also propose - a novel mechanism that improves translation quality for long sentences and in low-resource scenarios. |
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Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)
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| Challenge: | Neural networks equipped with self-attention have parallelizable computation and the ability to capture both long-range and local dependencies. |
| Approach: | They propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" it captures pairwise and global dependencies by a compatibility function composed of dot-product and additive attentions . |
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Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models (2021.acl-long)
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| Challenge: | Recent work has shown that convolutions have been successful in natural language learning. |
| Approach: | They propose a convolutional approach to construct relative position embeddings in self-attention layers and propose 'compact attention' they propose multiple ways to integrate convolutions into Transformer self- attention. |
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Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)
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| Challenge: | Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization. |
| Approach: | They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset. |
| Outcome: | The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters. |