arXiv:2303.12788
공식 코드 · Papers with Code
Open-source Frame Semantic Parsing
원문 초록
While the state-of-the-art for frame semantic parsing has progressed dramatically in recent years, it is still difficult for end-users to apply state-of-the-art models in practice. To address this, we present Frame Semantic Transformer, an open-source Python library which achieves near state-of-the-art performance on FrameNet 1.7, while focusing on ease-of-use. We use a T5 model fine-tuned on Propbank and FrameNet exemplars as a base, and improve performance by using FrameNet lexical units to provide hints to T5 at inference time. We enhance robustness to real-world data by using textual data augmentations during training.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Where, What, Why: Towards Explainable Driver Attention Prediction
Sigma: A dataset for text-to-code semantic parsing with statistical analysis
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing
Disambiguate First Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing
LP-LM: No Hallucinations in Question Answering with Logic Programming
Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.