Datasets:
Tasks:
Text2Text Generation
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
License:
language: | |
- en | |
license: cc-by-sa-4.0 | |
size_categories: | |
- 10M<n<100M | |
task_categories: | |
- text2text-generation | |
pretty_name: WikiSplit++ | |
dataset_info: | |
features: | |
- name: id | |
dtype: int64 | |
- name: complex | |
dtype: string | |
- name: simple_reversed | |
dtype: string | |
- name: simple_tokenized | |
sequence: string | |
- name: simple_original | |
dtype: string | |
- name: entailment_prob | |
dtype: float64 | |
- name: split | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 380811358.0 | |
num_examples: 504375 | |
- name: validation | |
num_bytes: 47599265.0 | |
num_examples: 63065 | |
- name: test | |
num_bytes: 47559833.0 | |
num_examples: 62993 | |
download_size: 337857760 | |
dataset_size: 475970456.0 | |
configs: | |
- config_name: default | |
data_files: | |
- split: train | |
path: data/train-* | |
- split: validation | |
path: data/validation-* | |
- split: test | |
path: data/test-* | |
# WikiSplit++ | |
This dataset is the HuggingFace version of WikiSplit++. | |
WikiSplit++ enhances the original WikiSplit by applying two techniques: filtering through NLI classification and sentence-order reversing, which help to remove noise and reduce hallucinations compared to the original WikiSplit. | |
The preprocessed WikiSplit dataset that formed the basis for this can be found [here](https://huggingface.co/datasets/cl-nagoya/wikisplit). | |
## Dataset Description | |
- **Repository:** https://github.com/nttcslab-nlp/wikisplit-pp | |
- **Paper:** https://arxiv.org/abs/2404.09002 | |
- **Point of Contact:** [Hayato Tsukagoshi](mailto:tsukagoshi.hayato.r2@s.mail.nagoya-u.ac.jp) | |
## Usage | |
```python | |
import datasets as ds | |
dataset: ds.DatasetDict = ds.load_dataset("cl-nagoya/wikisplit-pp", split="train") | |
print(dataset) | |
# DatasetDict({ | |
# train: Dataset({ | |
# features: ['id', 'complex', 'simple_reversed', 'simple_tokenized', 'simple_original', 'entailment_prob', 'split'], | |
# num_rows: 504375 | |
# }) | |
# validation: Dataset({ | |
# features: ['id', 'complex', 'simple_reversed', 'simple_tokenized', 'simple_original', 'entailment_prob', 'split'], | |
# num_rows: 63065 | |
# }) | |
# test: Dataset({ | |
# features: ['id', 'complex', 'simple_reversed', 'simple_tokenized', 'simple_original', 'entailment_prob', 'split'], | |
# num_rows: 62993 | |
# }) | |
# }) | |
``` | |
### Data Fields | |
- id: The ID of the data (note that it is not compatible with the existing WikiSplit) | |
- complex: A complex sentence | |
- simple_reversed: Simple sentences with their order reversed | |
- simple_tokenized: A list of simple sentences split by [PySBD](https://github.com/nipunsadvilkar/pySBD), not reversed in order (often consists of 2 elements) | |
- simple_original: Simple sentences in their original order | |
- entailment_prob: The average probability that each simple sentence is classified as an entailment according to the complex sentence. [DeBERTa-xxl](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli) is used for the NLI classification. | |
- split: Indicates which split (train, val, or tune) this data belonged to in the original WikiSplit dataset | |
## Paper | |
Tsukagoshi et al., [WikiSplit++: Easy Data Refinement for Split and Rephrase](https://arxiv.org/abs/2404.09002), LREC-COLING 2024. | |
## Abstract | |
The task of Split and Rephrase, which splits a complex sentence into multiple simple sentences with the same meaning, improves readability and enhances the performance of downstream tasks in natural language processing (NLP). | |
However, while Split and Rephrase can be improved using a text-to-text generation approach that applies encoder-decoder models fine-tuned with a large-scale dataset, it still suffers from hallucinations and under-splitting. | |
To address these issues, this paper presents a simple and strong data refinement approach. | |
Here, we create WikiSplit++ by removing instances in WikiSplit where complex sentences do not entail at least one of the simpler sentences and reversing the order of reference simple sentences. | |
Experimental results show that training with WikiSplit++ leads to better performance than training with WikiSplit, even with fewer training instances. | |
In particular, our approach yields significant gains in the number of splits and the entailment ratio, a proxy for measuring hallucinations. | |
## License | |
[WikiSplit](https://github.com/google-research-datasets/wiki-split) is distributed under the CC-BY-SA 4.0 license. | |
This dataset follows suit and is distributed under the CC-BY-SA 4.0 license. | |