Datasets:

cl-nagoya
/

wikisplit-pp

	---
	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.