Challenge: Currently, attention-based models face computational hurdles in processing long sequences due to its quadratic complexity.
Approach: They propose a conformer whose encoder self-attentions are replaced with Hyena for speech processing . they propose 'confhyena' model that reduces training time by 27% at minimal cost .
Outcome: The proposed model reduces training time by 27% at the cost of minimal quality degradation.

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Challenge: Existing linearization frameworks that rely on softmax attention with quadratic time and memory complexity pose significant computational and memory bottlenecks for long-context applications.
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Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)

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Challenge: Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling.
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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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Adaptive Attention Span in Transformers (P19-1)

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Challenge: We extend the maximum context size of a neural network called Transformer to 8k characters.
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How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures (P18-1)

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Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)

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Challenge: Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy .
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On the Locality of Attention in Direct Speech Translation (2022.acl-srw)

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Challenge: Recent advances in NLP have created problems with the complexity of the self-attention layer.
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Attention-based Contextual Language Model Adaptation for Speech Recognition (2021.findings-acl)

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Challenge: Existing language models do not incorporate utterance level contextual information . however, for some domains like voice assistants, additional context provides a rich input signal .
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Exploring Attention Attractors in Large Language Models (2026.acl-long)

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Challenge: Existing studies have suggested that attention attractors function as "summary tokens" while others speculate that tokens with weaker semantics attract high attention, they act as attention sinks that offload excessive attention.
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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.
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