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YAML Metadata Warning: The task_categories "simplification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other
YAML Metadata Warning: The task_ids "unknown" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering

Dataset Card for GEM/BiSECT

Link to Main Data Card

You can find the main data card on the GEM Website.

Dataset Summary

This dataset is composed of 1 million complex sentences with the task to split and simplify them while retaining the full meaning. Compared to other simplification corpora, BiSECT requires more significant edits. BiSECT offers splits in English, German, French, and Spanish.

You can load the dataset via:

import datasets
data = datasets.load_dataset('GEM/BiSECT')

The data loader can be found here.





Dataset Overview

Where to find the Data and its Documentation








    title = "{B}i{SECT}: Learning to Split and Rephrase Sentences with Bitexts",
    author = "Kim, Joongwon  and
      Maddela, Mounica  and
      Kriz, Reno  and
      Xu, Wei  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "6193--6209"

Contact Name

Joongwon Kim, Mounica Maddela, Reno Kriz

Contact Email,,

Has a Leaderboard?


Languages and Intended Use



Covered Languages

English, German, French, Spanish, Castilian


other: Other license

Intended Use

Split and Rephrase.

Add. License Info

The dataset is not licensed by itself, and the source OPUS data consists solely of publicly available parallel corpora.

Primary Task


Communicative Goal

To rewrite a long, complex sentence into shorter, readable, meaning-equivalent sentences.


Dataset Structure

Data Fields

  • gem_id (string): a unique identifier for the instance
  • source_sentence (string): sentence to be simplified
  • target_sentence (string)" simplified text that was split and rephrased

Example Instance

   "gem_id": "bisect-train-0",
   "source_sentence": "The report on the visit to Bhutan states that the small community has made the task of coordination less complex and success is manifested in the synchronized programming cycles which now apply to all but one of the agencies ( the World Health Organization ) .",
   "target_sentence": "The report on the visit to Bhutan says that the small community has made the coordination work less complex . Success manifests itself in synchronized programming cycles that now apply to all but one organism ( the World Health Organization ) ."

Data Splits

For the main English BiSECT dataset, the splits are as follows: 1. Train (n=928440) 2. Validation (n=9079) 3. Test (n=583) Additional challenge sets were derived from the data presented in the paper. Please refer to the challenge set sections. The train/validation/test splits for other languages are as follows: German (n=184638/n=864/n=735) Spanish (n=282944/n=3638/n=3081) French (n=491035/n=2400/n=1036)

Splitting Criteria

While all training data were derived from subsets of the OPUS corpora, different source subsets were used for training v.s., validation and testing. The training set comprised more web crawl data, whereas development and test sets comprised EMEA and EU texts. Details can be found in the BiSECT paper.

Dataset in GEM

Rationale for Inclusion in GEM

Why is the Dataset in GEM?

Understanding long and complex sentences is challenging for both humans and NLP models. The BiSECT dataset helps facilitate more research on Split and Rephrase as a task within itself, as well as how it can benefit downstream NLP applications.

Similar Datasets


Unique Language Coverage


Difference from other GEM datasets

BiSECT is the largest available corpora for the Split and Rephrase task. In addition, it has been shown that BiSECT is of higher quality than previous Split and Rephrase corpora and contains a wider variety of splitting operations.

Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning.

GEM-Specific Curation

Modificatied for GEM?


GEM Modifications

data points added

Modification Details

The original BiSECT training, validation, and test splits are maintained to ensure a fair comparison. Note that the original BiSECT test set was created by manually selecting 583 high-quality Split and Rephrase instances from 1000 random source-target pairs sampled from the EMEA and JRC-Acquis corpora from OPUS.

As the first challenge set, we include the HSPLIT-Wiki test set, containing 359 pairs. For each complex sentence, there are four reference splits; To ensure replicability, as reference splits, we again follow the BiSECT paper and present only the references from HSplit2-full.

In addition to the two evaluation sets used in the original BiSECT paper, we also introduce a second challenge set. For this, we initially consider all 7,293 pairs from the EMEA and JRC-Acquis corpora. From there, we classify each pair using the classification algorithm from Section 4.2 of the original BiSECT paper. The three classes are as follows:

  1. Direct Insertion: when a long sentence l contains two independent clauses and requires only minor changes in order to make a fluent and meaning-preserving split s.
  2. Changes near Split, when l contains one independent and one dependent clause, but modifications are restricted to the region where l is split.
  3. Changes across Sentences, where major changes are required throughout l in order to create a fluent split s. We keep only pairs labeled as Type 3, and after filtering out pairs with significant length differences (signaling potential content addition/deletion), we present a second challenge set of 1,798 pairs.

Additional Splits?


Getting Started with the Task

Pointers to Resources

The dataset can be downloaded from the original repository by the authors.

The original BiSECT paper proposes several transformer-based models that can be used as baselines, which also compares against Copy512, an LSTM-based model and the previous state-of-the-art.

The common metric used for automatic evaluation of Split and Rephrase, and sentence simplification more generally is SARI. The BiSECT paper also evaluates using BERTScore. Note that automatic evaluations tend to not correlate well with human judgments, so a human evaluation for quality is generally expected for publication. The original BiSECT paper provides templates for collecting quality annotations from Amazon Mechanical Turk.

Previous Results

Previous Results

Measured Model Abilities

Text comprehension (needed to generate meaning-equivalent output) and notions of complexity (what is more 'readable' in terms of syntactic structure, lexical choice, punctuation).


Other: Other Metrics, BERT-Score

Other Metrics

SARI is a metric used for evaluating automatic text simplification systems. The metric compares the predicted simplified sentences against the reference and the source sentences. It explicitly measures the goodness of words that are added, deleted and kept by the system.

Proposed Evaluation

Existing automatic metrics, such as BLEU (Papineni et al., 2002) and SAMSA (Sulem et al., 2018), are not optimal for the Split and Rephrase task as they rely on lexical overlap between the output and the target (or source) and underestimate the splitting capability of the models that rephrase often.

As such, the dataset creators focused on BERTScore (Zhang et al., 2020) and SARI (Xu et al., 2016). BERTScore captures meaning preservation and fluency well (Scialom et al., 2021). SARI can provide three separate F1/precision scores that explicitly measure the correctness of inserted, kept and deleted n-grams when compared to both the source and the target. The authors used an extended version of SARI that considers lexical paraphrases of the reference.

Previous results available?


Dataset Curation

Original Curation

Original Curation Rationale

BiSECT was constructed to satisfy the need of a Split and Rephrase corpus that is both large-scale and high-quality. Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning.

Communicative Goal

The goal of Split and Rephrase is to break down longer sentences into multiple shorter sentences, which has downstream applications for many NLP tasks, including machine translation and dependency parsing.

Sourced from Different Sources


Language Data

How was Language Data Obtained?


Where was it found?


Language Producers


Topics Covered

There is a range of topics spanning domains such as web crawl and government documents (European Parliament, United Nations, EMEA).

Data Validation

validated by data curator

Data Preprocessing

The construction of the BiSECT corpus relies on leveraging the sentence-level alignments from OPUS), a collection of bilingual parallel corpora over many language pairs. Given a target language A, this work extracts all 1-2 and 2-1 sentence alignments from parallel corpora between A and a set of foreign languages B.

Next, the foreign sentences are translated into English using Google Translate’s Web API service to obtain sentence alignments between a single long sentence and two corresponding split sentences, both in the desired language.

The authors further filtered the data in a hybrid fashion.

Was Data Filtered?


Filter Criteria

To remove noise, the authors remove pairs where the single long sentence (l) contains a token with a punctuation after the first two and before the last two alphabetic characters. The authors also removed instances where l contains more than one unconnected component in its dependency tree, generated via SpaCy.

Structured Annotations

Additional Annotations?


Annotation Service?



Any Consent Policy?


Justification for Using the Data

Since this data is collected from OPUS, all instances are already in the public domain.

Private Identifying Information (PII)

Contains PII?


Categories of PII

generic PII

Any PII Identification?

no identification


Any Maintenance Plan?


Broader Social Context

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data


Impact on Under-Served Communities

Addresses needs of underserved Communities?


Details on how Dataset Addresses the Needs

The data as provided in GEMv2 is in English, which is a language with abundant existing resources. However, the original paper also provides Split and Rephrase pairs for French, Spanish, and German, while providing a framework for leveraging bilingual corpora from any language pair found within OPUS.

Discussion of Biases

Any Documented Social Biases?


Are the Language Producers Representative of the Language?

The language produced in the dataset is limited to what is captured in the used subset of the OPUS corpora, which might not represent the full distribution of speakers from all locations. For example, the corpora used are from a limited set of relatively formal domains, so it is possible that high performance on the BiSECT test set may not transfer to more informal text.

Considerations for Using the Data

PII Risks and Liability

Potential PII Risk

Since this data is collected from OPUS, all pairs are already in the public domain.


Copyright Restrictions on the Dataset

public domain

Copyright Restrictions on the Language Data

public domain

Known Technical Limitations

Technical Limitations

The creation of English BiSECT relies on translating non-English text back to English. While machine translation systems tend to perform well on high-resource languages, there is still a non-negligible chance that there these systems make errors; through a manual evaluation of a subset of BiSECT, it was found that 15% of pairs contained significant errors, while an additional 22% contained minor adequacy/fluency errors. This problem is exacerbated slightly when creating German BiSECT (22% significant errors, 24% minor errors), and these numbers would likely get larger if lower-resource languages were used.

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