Datasets:
File size: 4,862 Bytes
c097d73 ea9437b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
---
license: mit
language:
- en
configs:
- config_name: var-01
data_files:
- split: train
path: var-01/train.jsonl
- split: dev
path: var-01/dev.jsonl
- split: test
path: var-01/test.jsonl
- split: train_mix
path: var-01/train_mix.jsonl
- config_name: var-02
data_files:
- split: train
path: var-02/train.jsonl
- split: dev
path: var-02/dev.jsonl
- split: test
path: var-02/test.jsonl
- split: train_mix
path: var-02/train_mix.jsonl
- config_name: var-03
data_files:
- split: train
path: var-03/train.jsonl
- split: dev
path: var-03/dev.jsonl
- split: test
path: var-03/test.jsonl
- split: train_mix
path: var-03/train_mix.jsonl
- config_name: var-04
data_files:
- split: train
path: var-04/train.jsonl
- split: dev
path: var-04/dev.jsonl
- split: test
path: var-04/test.jsonl
- split: train_mix
path: var-04/train_mix.jsonl
- config_name: var-05
data_files:
- split: train
path: var-05/train.jsonl
- split: dev
path: var-05/dev.jsonl
- split: test
path: var-05/test.jsonl
- split: train_mix
path: var-05/train_mix.jsonl
- config_name: var-06
data_files:
- split: train
path: var-06/train.jsonl
- config_name: var-07
data_files:
- split: train
path: var-07/train.jsonl
- config_name: var-08
data_files:
- split: train
path: var-08/train.jsonl
- config_name: var-09
data_files:
- split: train
path: var-09/train.jsonl
---
# Re-DocRED-CF
Many datasets have been developed to train and evaluate document-level relation extraction (RE) models. Most of these are constructed using real-world data. However, it has been shown that RE models trained on real-world data suffer from factual biases. To evaluate and address this issue, we present [**CovEReD** (Paper)](https://www.arxiv.org/abs/2407.06699), a counterfactual data generation approach for document-level relation extraction datasets through entity replacement.
Using our pipeline, we have generated **Re-DocRED-CF**, a dataset of counterfactual RE documents, to help evaluate and address inconsistencies in document-level RE.
This repo contains five counterfactual variations of the seed dataset, i.e., Re-DocRED. All five sets of train/dev/test dataset files are available here through the HuggingFace Datasets API 🤗.
To select a specific variation (e.g. `var-01`):
```python
dataset = load_dataset("amodaresi/Re-DocRED-CF", "var-01")
```
#### Output:
```python
DatasetDict({
train: Dataset({
features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
num_rows: 2870
})
dev: Dataset({
features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
num_rows: 466
})
test: Dataset({
features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
num_rows: 453
})
train_mix: Dataset({
features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
num_rows: 5923
})
})
```
The `train_mix` is the original training set combined with its counterfactual variation counterpart.
We have also included four additional training set variations (var-[06, 07, 08, 09]), though they were not used in the evaluations presented in our paper.
The properties `title`, `labels`, `vertexSet`, and `sents` are structured similarly to those in the original DocRED & Re-DocRED datasets:
- `title`: Document title.
- `labels`: List of relations. Each entry indicates the relation between a head and a tail entity, with some entries also specifying evidence sentences.
- `vertexSet`: List of entity vertex sets. Each entry represents a vertex specifying all mentions of an entity by their position in the document, along with their type.
- `sents`: Tokenized sentences.
In examples that are counterfactually generated, the title includes a variation number. For example: `AirAsia Zest ### 1`.
The `original_doc_id` denotes the index of the example in the original seed dataset, i.e., Re-DocRED.
## GitHub Repo & Paper
For more information about the **CovEReD** pipeline, refer to:
- 📄 Paper: "[Consistent Document-Level Relation Extraction via Counterfactuals](https://www.arxiv.org/abs/2407.06699)"
- 🔗 GitHub Repo: [https://github.com/amodaresi/CovEReD](https://github.com/amodaresi/CovEReD)
## Cite
If you use the dataset, **CovEReD** pipeline, or code from this repository, please cite the paper:
```bibtex
@inproceedings{modarressi-covered-2024,
title="Consistent Document-Level Relation Extraction via Counterfactuals",
author="Ali Modarressi and Abdullatif Köksal and Hinrich Schütze",
year="2024",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
address = "Miami, United States",
publisher = "Association for Computational Linguistics",
}
``` |