arXiv:1908.11571
Hierarchical Pointer Net Parsing
원문 초록
Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Making Language Model a Hierarchical Classifier and Generator
Mitigating Object Hallucinations via Sentence-Level Early Intervention
토론
이 논문과 연결된 포럼 글이 아직 없습니다.