Code and data for the NAACL 2022 paper Few-Shot Document-Level Relation Extraction
- Accessing the data (FREDo benchmark tasks)
- Dependencies
- Reproducing Results (+ link to trained models)
- Relevant Code per Section in Paper
- Bibtex for Citing
If you are only interested in the raw data for the tasks, you can directly download sampled episodes here: https://drive.google.com/drive/folders/1PuJSJxqZP4ijxFSBZZ6Fmc0SgR2S8pYU?usp=sharing (test_episodes.zip [~120 MB DL, ~550MB on disk] + train_episodes_single.zip [~330MB DL, ~1.4GB on disk]+ train_episodes_schema.zip [~330MB DL, ~1.4GB on disk])
Training and dev/validation episodes are available in two different versions: "single" or "schema". For "single", each episode contains annotations for only a single relation type. For "schema", each episode contains annotations for multiple relation types sampled via the same procedure as the test episodes (see section 4.3 of paper).
In our paper and code we evaluate macro F1 scores across relation types.
- data/[train, dev, test_docred, test_scierc].json contain all annotated documents used for training and testing.
- data/*_indices.json contain sampled episodes (only the indices of the documents and which relations are to be annotated/extracted).
You can either use our pipeline or export all episodes as json files to use in your own pipeline.
Test episode sampling is called in train.py (Lines 72+73).
If you are evaluating your own model, please use the test episodes as sampled by us. You can run export_test_episodes.py to export all episodes for use with your own pipeline.
Dependencies:
- torch (1.8.0)
- transformers (4.18.0)
- tqdm (4.64.0)
- wandb (0.12.16)
python3 -m venv venv
source venv/bin/activate
pip install wheel
pip install -U setuptools
pip install torch==1.8.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers tqdm wandb
In order to reproduce the results from the paper, you can download the models here: https://drive.google.com/drive/folders/1eulLgrGiOwSZawoOGytA6fJZ-b3Rcrob?usp=sharing
The following are the commands for reporducing the results in the paper. Click on "Expected output" for detailed results.
python train.py --support_docs_eval 1 --model dlmnav+sie+sbn --num_epochs 0 --use_markers False
Expected output
3nX3120z6X
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P361 0.20 1.79 0.37 893
P279 0.02 5.15 0.03 136
P102 0.42 14.49 0.82 428
P17 3.76 11.08 5.61 59312
P35 0.29 12.61 0.56 222
P495 0.41 10.08 0.79 1081
P463 0.09 1.62 0.18 493
P3373 0.18 16.43 0.36 645
P118 0.13 8.30 0.25 253
P39 0.03 2.63 0.07 38
P140 0.12 13.97 0.24 458
P272 0.07 10.00 0.13 160
P674 0.28 7.76 0.54 348
P25 0.16 19.28 0.31 83
P364 0.03 16.92 0.06 65
P1001 0.14 10.13 0.27 375
P194 0.07 8.56 0.14 222
P582 0.07 3.96 0.14 101
- - - - -
macro 0.36 9.71 0.60
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 3.60 3.82 3.71 42142
PART-OF 0.46 1.90 0.74 2529
CONJUNCTION 1.92 5.24 2.81 6414
EVALUATE-FOR 1.22 2.79 1.70 4223
HYPONYM-OF 1.30 3.64 1.92 4482
COMPARE 0.54 2.49 0.89 2328
FEATURE-OF 0.33 1.30 0.53 2465
- - - - -
macro 1.34 3.03 1.76
python train.py --support_docs_eval 3 --model dlmnav+sie+sbn --num_epochs 0 --use_markers False
Expected output
G7BwlKpec6
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P17 5.19 6.11 5.61 33229
P361 0.50 1.80 0.78 887
P272 0.14 20.00 0.27 55
P495 0.69 6.35 1.24 850
P674 0.45 8.59 0.86 128
P35 0.32 2.70 0.56 111
P102 0.76 15.77 1.45 279
P140 0.23 2.34 0.42 171
P364 0.32 19.23 0.63 78
P463 0.34 3.11 0.61 193
P3373 0.37 7.88 0.71 241
P194 0.21 8.62 0.41 116
P1001 0.20 7.78 0.39 167
P279 0.04 8.47 0.08 59
P25 0.03 5.26 0.05 19
P118 0.38 13.83 0.75 94
P582 0.14 5.56 0.27 36
P39 0.42 50.00 0.84 2
- - - - -
macro 0.60 10.75 0.89
---- SCIERC TEST EVAL -----
type precision recall f1 support
PART-OF 0.61 1.65 0.89 1335
USED-FOR 3.98 3.02 3.44 14548
HYPONYM-OF 2.16 1.56 1.81 2311
CONJUNCTION 3.07 4.53 3.66 3332
COMPARE 1.09 3.36 1.64 1162
EVALUATE-FOR 1.29 1.89 1.53 2382
FEATURE-OF 0.68 1.21 0.88 1235
- - - - -
macro 1.84 2.46 1.98
python train.py --support_docs_eval 1 --model dlmnav --num_epochs 0 --load_checkpoint checkpoints/best_dlmnav.pt
Expected output
38BE1Nj8Pu
loading model from checkpoints/best_dlmnav.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P279 0.37 2.94 0.66 136
P17 30.40 2.38 4.41 59312
P463 3.82 8.11 5.19 493
P495 11.43 16.47 13.50 1081
P674 4.45 23.56 7.49 348
P361 3.33 5.04 4.01 893
P364 1.76 36.92 3.36 65
P39 0.27 15.79 0.52 38
P35 5.06 27.48 8.54 222
P3373 7.12 21.24 10.67 645
P118 15.54 62.06 24.86 253
P1001 1.93 6.40 2.96 375
P194 3.90 30.63 6.92 222
P25 2.03 45.78 3.89 83
P102 10.61 14.72 12.33 428
P272 4.47 47.50 8.17 160
P582 3.13 27.72 5.62 101
P140 9.24 10.04 9.62 458
- - - - -
macro 6.60 22.49 7.37
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 2.54 0.04 0.08 42142
PART-OF 0.59 0.36 0.44 2529
FEATURE-OF 0.32 0.16 0.22 2465
CONJUNCTION 1.06 0.25 0.40 6414
EVALUATE-FOR 0.91 0.19 0.31 4223
HYPONYM-OF 6.25 2.57 3.64 4482
COMPARE 1.31 0.69 0.90 2328
- - - - -
macro 1.85 0.61 0.86
python train.py --support_docs_eval 3 --model dlmnav --num_epochs 0 --load_checkpoint checkpoints/best_dlmnav.pt
Expected output
eASm3VD2Bv
loading model from checkpoints/best_dlmnav.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P17 32.74 1.66 3.17 33229
P361 4.51 4.51 4.51 887
P674 5.35 31.25 9.14 128
P495 11.91 14.94 13.26 850
P140 7.08 8.77 7.83 171
P39 0.00 0.00 0.00 2
P463 3.45 6.22 4.44 193
P194 6.65 31.03 10.96 116
P35 4.64 27.03 7.93 111
P364 10.21 62.82 17.56 78
P272 4.55 58.18 8.44 55
P582 4.36 38.89 7.84 36
P1001 1.67 2.40 1.97 167
P118 16.35 45.74 24.09 94
P279 1.05 5.08 1.73 59
P25 1.78 47.37 3.43 19
P102 12.29 15.41 13.67 279
P3373 10.77 26.14 15.25 241
- - - - -
macro 7.74 23.75 8.62
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 0.00 0.00 0.00 14548
PART-OF 0.00 0.00 0.00 1335
HYPONYM-OF 8.76 1.69 2.83 2311
CONJUNCTION 0.65 0.06 0.11 3332
COMPARE 0.94 0.26 0.41 1162
EVALUATE-FOR 1.73 0.25 0.44 2382
FEATURE-OF 0.00 0.00 0.00 1235
- - - - -
macro 1.73 0.32 0.54
python train.py --support_docs_eval 1 --model dlmnav+sie --num_epochs 0 --load_checkpoint checkpoints/best_sie_sbn.pt
Expected output
vxMctq1z4F
loading model from checkpoints/best_sie_sbn.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P279 0.52 2.94 0.89 136
P17 22.84 3.44 5.98 59312
P463 3.27 11.56 5.10 493
P495 5.14 2.68 3.53 1081
P674 5.90 22.99 9.38 348
P1001 6.67 17.60 9.68 375
P35 6.17 22.52 9.68 222
P39 0.33 18.42 0.64 38
P361 3.04 2.80 2.92 893
P364 2.60 35.38 4.85 65
P25 1.85 48.19 3.56 83
P3373 5.87 25.89 9.58 645
P118 11.25 77.08 19.63 253
P102 6.71 26.17 10.68 428
P582 2.59 17.82 4.52 101
P194 4.78 13.96 7.13 222
P272 5.31 44.38 9.49 160
P140 6.44 13.76 8.77 458
- - - - -
macro 5.63 22.64 7.00
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 6.35 1.73 2.72 42142
PART-OF 0.68 0.51 0.59 2529
CONJUNCTION 1.61 0.72 0.99 6414
FEATURE-OF 0.16 0.12 0.14 2465
EVALUATE-FOR 1.86 1.18 1.45 4223
HYPONYM-OF 3.76 1.43 2.07 4482
COMPARE 2.36 1.42 1.77 2328
- - - - -
macro 2.40 1.02 1.39
python train.py --support_docs_eval 3 --model dlmnav+sie --num_epochs 0 --load_checkpoint checkpoints/best_sie_sbn.pt
Expected output
Ta4ae83La8
loading model from checkpoints/best_sie_sbn.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P361 3.92 3.49 3.69 887
P674 5.29 42.97 9.42 128
P17 24.60 5.30 8.72 33229
P495 3.57 2.47 2.92 850
P140 4.95 13.45 7.23 171
P102 6.39 25.09 10.18 279
P364 4.61 60.26 8.56 78
P463 3.39 14.51 5.49 193
P39 0.05 50.00 0.11 2
P194 6.32 24.14 10.02 116
P1001 5.08 23.35 8.35 167
P279 1.24 22.03 2.35 59
P118 7.23 78.72 13.24 94
P272 3.62 63.64 6.85 55
P35 3.95 14.41 6.20 111
P3373 6.77 32.78 11.22 241
P582 2.58 36.11 4.82 36
P25 1.21 73.68 2.37 19
- - - - -
macro 5.26 32.58 6.76
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 6.37 3.62 4.61 14548
HYPONYM-OF 5.14 2.34 3.21 2311
FEATURE-OF 0.36 0.40 0.38 1235
PART-OF 0.74 0.82 0.78 1335
EVALUATE-FOR 1.57 1.22 1.37 2382
CONJUNCTION 3.14 1.98 2.43 3332
COMPARE 2.78 2.58 2.68 1162
- - - - -
macro 2.87 1.85 2.21
python train.py --support_docs_eval 1 --model dlmnav+sie+sbn --num_epochs 0 --load_checkpoint checkpoints/best_sie_sbn.pt
Expected output
HXGmfwnstC
loading model from checkpoints/best_sie_sbn.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P361 0.33 3.14 0.59 893
P279 0.04 1.47 0.07 136
P17 8.89 20.26 12.35 59312
P35 0.72 23.42 1.40 222
P495 1.47 25.16 2.78 1081
P463 0.36 14.00 0.71 493
P674 0.40 31.03 0.79 348
P3373 0.98 43.72 1.92 645
P1001 0.62 29.60 1.22 375
P194 0.45 32.88 0.88 222
P118 0.42 34.78 0.84 253
P272 0.69 36.25 1.36 160
P39 0.01 13.16 0.02 38
P582 0.23 15.84 0.46 101
P102 0.90 49.30 1.76 428
P140 0.23 12.66 0.44 458
P364 0.06 15.38 0.12 65
P25 0.40 21.69 0.78 83
- - - - -
macro 0.96 23.54 1.58
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 4.62 5.14 4.86 42142
PART-OF 0.58 1.82 0.88 2529
CONJUNCTION 3.30 8.53 4.76 6414
FEATURE-OF 0.39 1.74 0.63 2465
EVALUATE-FOR 2.08 4.10 2.76 4223
HYPONYM-OF 2.22 4.46 2.97 4482
COMPARE 1.74 4.34 2.48 2328
- - - - -
macro 2.13 4.30 2.76
python train.py --support_docs_eval 3 --model dlmnav+sie+sbn --num_epochs 0 --load_checkpoint checkpoints/best_sie_sbn.pt
Expected output
AmcBdjBC9r
loading model from checkpoints/best_sie_sbn.pt
---- INDOMAIN TEST EVAL -----
type precision recall f1 support
P17 11.96 17.46 14.20 33229
P361 0.66 3.83 1.13 887
P495 2.63 16.12 4.52 850
P272 0.88 32.73 1.72 55
P279 0.11 5.08 0.22 59
P140 1.24 12.28 2.25 171
P102 2.10 48.03 4.02 279
P39 0.01 50.00 0.01 2
P674 1.24 25.00 2.37 128
P364 0.66 34.62 1.30 78
P463 0.33 6.22 0.63 193
P35 0.41 5.41 0.77 111
P194 1.43 30.17 2.73 116
P1001 1.88 29.94 3.54 167
P118 0.62 28.72 1.21 94
P3373 3.96 42.32 7.25 241
P582 0.34 13.89 0.66 36
P25 0.37 21.05 0.72 19
- - - - -
macro 1.71 23.49 2.74
---- SCIERC TEST EVAL -----
type precision recall f1 support
USED-FOR 6.06 5.69 5.87 14548
HYPONYM-OF 4.48 3.81 4.12 2311
FEATURE-OF 0.46 0.97 0.62 1235
PART-OF 0.85 1.87 1.17 1335
CONJUNCTION 5.63 9.12 6.96 3332
EVALUATE-FOR 3.12 3.57 3.33 2382
COMPARE 2.83 4.65 3.52 1162
- - - - -
macro 3.35 4.24 3.66
Covered by parse_test() in src/data.py (lines 138-324)
Covered in src/models/base_model.py Covered in src/models/dlmnav_sbn.py
Run model using:
python train.py --query_docs_train 3 --query_docs_eval 3 --support_docs_train 1 --support_docs_eval 1 --model dlmnav+sie+sbn --num_epochs 0 --use_markers False
Covered in src/models/base_model.py Covered in src/models/dlmnav_sbn.py
Train model using:
python train.py --query_docs_train 3 --query_docs_eval 3 --support_docs_train 1 --support_docs_eval 1 --model dlmnav
Covered in src/models/base_model.py Covered in src/models/dlmnav_sie.py
Train model using:
python train.py --query_docs_train 3 --query_docs_eval 3 --support_docs_train 1 --support_docs_eval 1 --model dlmnav+sie
Covered in src/models/base_model.py Covered in src/models/dlmnav_sbn.py
Train model using:
python train.py --query_docs_train 3 --query_docs_eval 3 --support_docs_train 1 --support_docs_eval 1 --model dlmnav+sie+sbn
Covered by parse_episodes() in src/data.py (lines 144-324)
In order to control sampling strategies, use the following parameters:
- --balancing_train hard/soft/single
- --balancing_dev hard/soft/single
The above parameters control how relation types are sampled for train/dev episodes. "hard" balances the annotated relation types so that the resulting episodes contain the same amount of examples for each relation type. "soft" corresponds to the balancing procedure used for the test episodes, which is outlined in section 4.3 of the paper. "single" annotates only a single relation type per document.
- --ensure_positive True/False
The above parameter controls whether the query documents contain at least a single positive example of the relation type r_s.
Parameters for test sets are found in train.py (lines 70-71)
If you use the benchmark or the code, please cite this paper:
@inproceedings{popovic_fsdlre_2022,
title = "{F}ew-{S}hot {D}ocument-{L}evel {R}elation {E}xtraction",
author = "Popovic, Nicholas and Färber, Michael",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
year = "2022"
}
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