Interpreting Positional Information in Perspective of Word Order (2023.acl-long)
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| Challenge: | Attention mechanism is a powerful and effective method utilized in natural language processing, but it is insensitive to positional information. |
| Approach: | They propose a weight concatenation operation to evaluate its efficacy in machine translation tasks. |
| Outcome: | The proposed operation can encode positional information and confirms our hypothesis. |
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| Challenge: | Using attention weights, we show that NMT models make alignment errors by relying on uninformative tokens from the source sequence. |
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On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
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Is Attention Interpretable? (P19-1)
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| Challenge: | Attention mechanisms have seen wide adoption in neural NLP models. |
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Self-Attention with Relative Position Representations (N18-2)
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| Challenge: | Recent approaches to sequence to sequence learning leverage recurrence, convolution, attention or combination of recurrent and convolutional neural networks. |
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Computation Mechanism Behind LLM Position Generalization (2025.acl-long)
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| Challenge: | Existing studies have explored how LLMs handle positional relevance, but how they handle it remains unexplored. |
| Approach: | They propose to enforce certain computational mechanisms to allow for the tolerance in position perturbations in large language models (LLMs) they also find a pattern in intermediate features that allows this effect to be observed . |
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Local Structure Matters Most: Perturbation Study in NLU (2022.findings-acl)
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| Challenge: | Recent research shows that neural models are insensitive to word-order perturbations, but other studies suggest that models learn some abstract notion of syntax. |
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Understanding Attention for Text Classification (2020.acl-main)
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| Challenge: | Existing studies have focused on whether local attention weights reflect the importance of input representations. |
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