# Open-source Frame Semantic Parsing

arXiv: https://arxiv.org/abs/2303.12788 · HTML: https://mlsanity.com/paper/2303.12788/

## 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 (most cited first, 7 of 7)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Learning Joint Semantic Parsers from Disjoint Data](https://mlsanity.com/paper/1804.05990/) (2018)
- [Open-Domain Frame Semantic Parsing Using Transformers](https://mlsanity.com/paper/2010.10998/) (2020)
- [A Double-Graph Based Framework for Frame Semantic Parsing](https://mlsanity.com/paper/2206.09158/) (2022)
- [Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News](https://mlsanity.com/paper/2301.06178/) (2023)

## Built on by (most cited first, 3 of 3)

- [FRASE: Structured Representations for Generalizable SPARQL Query Generation](https://mlsanity.com/paper/2503.22144/) (2025)
- [Enhancing Frame Detection with Retrieval Augmented Generation](https://mlsanity.com/paper/2502.12210/) (2025)
- [LOGICPO: Efficient Translation of NL-based Logical Problems to FOL using LLMs and Preference Optimization](https://mlsanity.com/paper/2506.18383/) (2025)

## 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.