| Challenge: | Currently, incremental transition-based parsers require that all inputs are visible from the beginning to extract good features from a limited local context. |
| Approach: | They propose a technique to maximize local features with an attention mechanism which works as context- dependent dynamic feature selection. |
| Outcome: | The proposed technique can extract features from a limited local context and is able to perform multilingual experiments and demon strate on local ambiguous points. |
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| Challenge: | Existing document-level neural machine translation methods use all context sentences in a fixed scope. |
| Approach: | They propose an approach to select dynamic context so that document-level neural machine translation models can utilize more useful selected context sentences. |
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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
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Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)
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| Challenge: | Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy. |
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Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
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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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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
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Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
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Enhancing Machine Translation with Dependency-Aware Self-Attention (2020.acl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from poor localization quality due to sequential dependencies within the target sentence. |
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Syntax-guided Localized Self-attention by Constituency Syntactic Distance (2022.findings-emnlp)
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Shengyuan Hou, Jushi Kai, Haotian Xue, Bingyu Zhu, Bo Yuan, Longtao Huang, Xinbing Wang, Zhouhan Lin
| Challenge: | Recent studies have shown that Transformers is implicitly learning syntactic information from data, albeit is highly dependent on the quality and scale of the training data. |
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