Open-source Frame Semantic Parsing
This addresses the difficulty for end-users in applying advanced frame semantic parsing models in practice, though it is incremental as it builds on existing methods like T5.
The paper tackles the problem of making state-of-the-art frame semantic parsing models accessible to end-users by developing an open-source library called Frame Semantic Transformer, which achieves near state-of-the-art performance on FrameNet 1.7 with a focus on ease-of-use.
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.