| 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. |
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| Challenge: | Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. |
| Approach: | They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition. |
| Outcome: | The proposed model outperforms both individual models and a standard hybrid model on a machine translation task. |
Convolutional Self-Attention Networks (N19-1)
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| 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. |
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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. |
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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 . |
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How does Attention Affect the Model? (2021.findings-acl)
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| Challenge: | Existing studies on the effectiveness of attention in NLP do not consider changes in semantic capability of different components. |
| Approach: | They propose a framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic. |
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Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention (2020.coling-main)
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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. |
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Fine-tune BERT with Sparse Self-Attention Mechanism (D19-1)
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| Challenge: | Existing sparse self-attention fine-tuning models have been used to improve sentiment analysis, question answering, and natural language inference tasks. |
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Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)
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| Challenge: | Empirical results show that attention mechanism can be improved from the energy consumption aspects. |
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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. |
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Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks (2024.lrec-main)
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| Challenge: | Attention pruning techniques have been developed to identify and exploit sparseness . previous work has taken pioneering steps to discover and explain the sparsity in attention patterns . |
| Approach: | They propose a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. |
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