Papers with position embedding
Absolute Position Embedding Learns Sinusoid-like Waves for Attention Based on Relative Position (2023.emnlp-main)
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| Challenge: | Attention weight is a clue to interpret how a Transformer-based model makes an inference. |
| Approach: | They analyze the mechanism behind the concentration of attention on nearby tokens . they find that attention in some heads is largely determined by relative positions . |
| Outcome: | The attention weights of the self-attention in a Transformer-based model are analyzed . the model can learn relationships between tokens while allowing parallelization, they show . |
Evaluating the Effects of Embedding with Speaker Identity Information in Dialogue Summarization (2022.lrec-1)
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| Challenge: | Existing methods for automatic dialogue summarization do not take into account speaker identity information, but instead use sinusoidal functions to embed speaker information at the less informative part of the position embedding. |
| Approach: | They propose to embed speaker identity information into a dialogue transcript encoder to address this issue and reduce the "who said what"-related errors. |
| Outcome: | The proposed method improves the convergence of the model in training and increases the average ROUGE scores of the generated summaries in comparison to existing methods. |
Exploiting Pre-Ordering for Neural Machine Translation (L18-1)
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| Challenge: | Existing studies have shown that Neural Machine Translation suffers from the problems that some source words are mistakenly translated for multiple times . |
| Approach: | They propose a pre-ordering approach to solve the under-translation problem by pre-ordnanced source sentences and position embedding to enhance monotone translation. |
| Outcome: | The proposed method significantly improves translation quality by 2.43 BLEU points on Chinese-to-English translation. |
Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on BERT and Segment Embeddings (2022.lrec-1)
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| Challenge: | Understanding why contextualized embeddings work is still an active area of research. |
| Approach: | They propose to use a BERT architecture to encode a sub-word, position and a segment embedding as input representations for each sub- word. |
| Outcome: | The proposed model performs well on single-sentence prediction tasks while swapping segment IDs in paired-sentent tasks. |
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending (2024.acl-long)
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| Challenge: | Existing models that use self-attention and position embedding have anomalous behavior that hinder long context window extrapolation. |
| Approach: | They propose a collinear constraint between Q and K to integrate RoPE and self-attention. |
| Outcome: | The proposed model integrates self-attention and position embedding into LLMs without fine-tuning. |
GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
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| Challenge: | a funder name refers to an agency, organization, or program providing financial support for the research. |
| Approach: | They propose a funding sentence classifier and a relation extraction framework to extract grant information from scientific articles. |
| Outcome: | The proposed framework outperforms state-of-the-art BERT-based RE baselines against the PubMed Central and arXiv test sets. |
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 . |
On the token distance modeling ability of higher RoPE attention dimension (2024.findings-emnlp)
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| Challenge: | Existing work on extending the context length of language models based on Rotary position embedding (RoPE) has shown promising results in capturing longer-range contextual information. |
| Approach: | They propose to use a hidden dimension of an attention head to investigate its contribution to capturing long-distance dependencies. |
| Outcome: | The proposed model can capture long-distance dependencies by extending the attention of a particular dimension of an attention head. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
Assessing the Ability of Self-Attention Networks to Learn Word Order (P19-1)
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| Challenge: | Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure. |
| Approach: | They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN. |
| Outcome: | The proposed task quantifies how well word order information learned by SAN and RNN is learned. |
Circuit Complexity Bounds for RoPE-based Transformer Architecture (2025.emnlp-main)
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| Challenge: | Recent studies provide the circuit complexity bounds to Transformer-like architectures. position embedding has emerged as a crucial technique in modern large language models. |
| Approach: | They propose to use position embedding to improve Transformer-like architectures by analyzing their circuits and analyzing the results. |
| Outcome: | The proposed model is able to solve canonical tasks without embedding positional information. |
A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
| Approach: | They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization . |
| Outcome: | The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models . |