| Challenge: | Various tools have been developed to visualize attention in NLP models, ranging from attention-matrix heatmaps to bipartite graph representations. |
| Approach: | They propose an open-source tool that visualizes attention at multiple scales and provides a unique perspective on the attention mechanism. |
| Outcome: | The proposed model outperforms OpenAI GPT-2 and BERT on several language modeling benchmarks. |
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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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How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention (2020.coling-main)
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| Challenge: | Recent work on multi-head attention mechanism shows heuristics and clues in analyzing various aspects of the mechanism. |
| Approach: | They propose to cluster attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features. |
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Dodrio: Exploring Transformer Models with Interactive Visualization (2021.acl-demo)
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| Challenge: | Recent research suggests the key may lie in multi-headed attention mechanism’s ability to learn and represent linguistic information. |
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Roles and Utilization of Attention Heads in Transformer-based Neural Language Models (2020.acl-main)
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| Challenge: | Sentence encoders based on transformer architectures have shown promising results on various natural language understanding tasks. |
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InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
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| Challenge: | Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. |
| Approach: | They propose a new formulation of attention via the lens of the kernel which allows us to understand individual components of Transformer's attention. |
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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 . |
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Telling BERT’s Full Story: from Local Attention to Global Aggregation (2021.eacl-main)
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| Challenge: | Recent work discouraging the use of attention distributions for explaining a model’s behaviour suggests that attention distribution can provide insights into local behaviour of attention heads. |
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VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers (2023.findings-emnlp)
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| Challenge: | Recent work in interpretability suggests we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary space, a transformation that makes them more human interpretable. |
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Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation (2022.emnlp-main)
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| Challenge: | Existing studies have investigated the multi-head self-attention mechanism of transformers. |
| Approach: | They propose to use a human-in-the-loop pipeline to discover task-specific attention patterns and inject them into transformer models to improve their accuracy. |
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