Improve Transformer Models with Better Relative Position Embeddings (2020.findings-emnlp)
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| Challenge: | Existing methods for generating position embeddings are not fully utilized in NLP tasks. |
| Approach: | They propose to generalize the absolute position embedding to a generalized relative position embedded method . they also propose to use the relative embeddable method to improve the accuracy of large models . |
| Outcome: | The proposed method improves accuracy on the SQuAD1.1 dataset compared to previous methods . it can be easily adopted as a drop-in replacement for improving accuracy of large models . |
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| Challenge: | Existing work on pre-trained Transformers has focused on learning the meaning of positions . Embedding the position information in the self-attention mechanism is also an indispensable factor in NLP . |
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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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| Challenge: | Existing studies have reported superiority of relative PEs in translation tasks. |
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| Challenge: | Attention weight is a clue to interpret how a Transformer-based model makes an inference. |
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| Challenge: | Existing Transformers that scale to long sequences are not compatible with relative position encoding. |
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| Challenge: | Existing approaches for positional dependencies do not satisfy all criteria for optimal position encoding. |
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The Curious Case of Absolute Position Embeddings (2022.findings-emnlp)
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| Challenge: | In natural language, it is not absolute position that matters, but relative position . et al., 2017) language models incorporate positional encodings that encode absolute (linear) word order. |
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Dynamic Position Encoding for Transformers (2022.coling-1)
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| Challenge: | In neural machine translation, the general task of translating is to reduce the input sentence into smaller units (also known as statistical phrases), select an optimal translation for each unit, and place them in the correct order. |
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How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (2022.tacl-1)
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| Challenge: | Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm . a novel decomposition of Transformer output embeddables is demonstrated . |
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Recurrent Positional Embedding for Neural Machine Translation (D19-1)
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| Challenge: | Existing translation systems that use positional embeddings only encode static order dependencies based on discrete numerical information, which may hinder the improvement of translation capacity. |
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