texture-frames · argument-extraction head
The argument-extraction stage of
texture-frames, a fast FrameNet
semantic-frame parser. Given a sentence with a marked trigger and its frame, it
finds the spans that fill the frame's roles (frame elements) and labels each.
It fine-tunes microsoft/deberta-v3-large
on FrameNet 1.7 with a detect-then-classify design — two heads on one
backbone, a single forward pass:
- Head A — span detection: a role-agnostic 3-class BIO tagger (
O/B/I), "is this token part of an argument?". Dense signal, arbitrary-length spans. - Head B — role classification: for each detected span, pool its tokens
(
start ⊕ end ⊕ mean) and classify into only the current frame's frame elements (plus aNULLreject class), masked via the lexicon.
The input carries the predicate marker and the frame's FE menu
({frame} [FE1; FE2; …] : … <t> {trigger} </t> …). A NULL-bias at inference
sets the precision/recall operating point.
This is one of three stages. Use it through the package rather than alone.
Usage
pip install git+https://github.com/texturejc/Texture_Frames
from texture_frames import FrameParser
parser = FrameParser()
for ann in parser.parse("The chef gave food to the customer ."):
print([(a.role, a.text) for a in ann.arguments])
# [('Donor', 'The chef'), ('Theme', 'food'), ('Recipient', 'to the customer')]
Files
| File | What |
|---|---|
args2_model.pt | model state_dict (backbone + detection + role heads) |
role2id.json | {role name → id} label map (incl. <NULL>) + base_model |
| tokenizer files | DeBERTa-v3 tokenizer with the <t> / </t> markers added |
Loading is handled by texture_frames.weights.load_args.
Results
Open-Sesame test split, weighted F1 (non-core FEs = 0.5):
| Metric | This head | T5 baseline |
|---|---|---|
| Argument F1 | 0.750 | 0.753 |
| Speed | single forward pass (~50–60 ms) | 3 beam-search passes |
Parity with the generative baseline while running ~4× faster. The encoder went 0.628 (flat BIO) → 0.712 (detect-then-classify) → 0.750 (+ WordNet augmentation) across redesigns.
Training
microsoft/deberta-v3-large, AdamW lr 1e-5, warmup 0.06, weight decay 0.01,
batch 16, max length 320, bf16, 6 epochs with WordNet synonym augmentation.
Data: FrameNet 1.7 (NLTK), Open-Sesame splits.
Licence
Code (the package): MIT. Weights: trained on FrameNet 1.7, which carries its own academic-use terms — review them before redistributing.
Citation
@software{texture_frames,
author = {Carney, James},
title = {texture-frames: a fast DeBERTa encoder FrameNet parser},
url = {https://github.com/texturejc/Texture_Frames},
year = {2026}
}
Builds on David Chanin's
frame-semantic-transformer;
thanks to the Berkeley FrameNet and Open-Sesame projects.