Open-source Frame Semantic Parsing
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 22 Mar 2023.
Builds on
7 of 23 references.
References · 7
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019BERT for Joint Intent Classification and Slot Fillingcited by 196
- 2017Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffoldcited by 33
- 2018Learning Joint Semantic Parsers from Disjoint Datacited by 12
- 2020Open-Domain Frame Semantic Parsing Using Transformerscited by 12
- 2022A Double-Graph Based Framework for Frame Semantic Parsingcited by 10
- 2023Bike Frames: Understanding the Implicit Portrayal of Cyclists in the Newscited by 1
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Citing papers per year: 2025: 3.
3 papers.