Open-source Frame Semantic Parsing

TL;DR

This paper introduces an open-source Python library called Frame Semantic Transformer that simplifies the application of advanced frame semantic parsing models, achieving near state-of-the-art performance with improved robustness and usability.

Contribution

It presents a user-friendly, open-source frame semantic parser based on T5, incorporating FrameNet lexical hints and data augmentation for better real-world performance.

Findings

Achieves near state-of-the-art performance on FrameNet 1.7

Enhances robustness with textual data augmentations

Focuses on ease-of-use for end-users

Abstract

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.