Papers with capturing long-range dependencies
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
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| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis (2025.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. |
| Approach: | They propose a method to enhance long-range dependencies between aspect and opinion words in ABSA by combining attention mechanisms with a syntax-based Graph Convolutional Network and a Mamba-Transformer module. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
A Transformer-based Approach for Source Code Summarization (2020.acl-main)
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| Challenge: | Generating a readable summary that describes the functionality of a program is known as source code summarization. |
| Approach: | They propose a Transformer model that uses a self-attention mechanism to capture long-range dependencies by encoding source code tokens relative to the code token position. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by a significant margin. |