Open-source Frame Semantic Parsing
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.
PDF AbstractTasks
Datasets
FrameNet
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
Absolute Position Encodings •
Adafactor •
Adam •
Attention Dropout •
BPE •
Dense Connections •
Dropout •
GELU •
GLU •
Inverse Square Root Schedule •
Label Smoothing •
Layer Normalization •
Library •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
SentencePiece •
Softmax •
T5 •
Transformer
