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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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- token-classification |
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- text-generation |
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language: |
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- en |
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tags: |
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- NLP |
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- Entailment |
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- NLI |
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- google-research-datasets |
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pretty_name: PropSegment |
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size_categories: |
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- 10K<n<100K |
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--- |
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# PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition |
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## Dataset Description |
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- **Homepage:** https://github.com/google-research-datasets/PropSegmEnt |
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- **Repository:** https://github.com/google-research-datasets/PropSegmEnt |
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- **Paper:** https://arxiv.org/abs/2212.10750 |
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- **Point of Contact:** [email protected] |
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### Dataset Summary |
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This is a reproduced (i.e. after web-crawling) and processed version of [the "PropSegment" dataset](https://github.com/google-research-datasets/PropSegmEnt) from Google Research. |
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Since the [`News`](https://github.com/google-research-datasets/NewSHead) portion of the dataset is released only via urls, we reconstruct the dataset by crawling. |
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Overall, ~96% of the dataset can be reproduced, and the rest ~4% either have url no longer valid, or sentences that have been edited (i.e. cannot be aligned with the orignial dataset). |
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PropSegment (Proposition-level Segmentation and Entailment) is a large-scale, human annotated dataset for segmenting English text into propositions, and recognizing proposition-level entailment relations --- whether a different, related document entails each proposition, contradicts it, or neither. |
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The original dataset features >45k human annotated propositions, i.e. individual semantic units within sentences, as well as >35k entailment labels between propositions and documents. |
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Check out more details in the [dataset paper](https://arxiv.org/abs/2212.10750). |
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## Dataset Structure |
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Here we provide processed versions of the dataset for seq2seq model inputs/outputs. |
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`proposition_segmentation.*.jsonl` contains data for the text segmentation task, i.e. split a sentence into propositions. |
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The output propositions are concatenated as one string (with no particular order between them) by a special token `[SEP]`. |
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Each proposition is annotated as spans enclosed by `[M]` and `[/M]`. |
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``` |
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{ |
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"sentence": "This film marks the directorial debut for production designer Robert Stromberg.", |
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"propositions": "This film marks the directorial debut for [M]production designer Robert Stromberg.[/M][SEP]This [M]film marks the directorial debut for[/M] production designer [M]Robert Stromberg[/M]." |
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} |
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``` |
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`propnli.*.jsonl` contains examples for the proposition-to-document entailment task, i.e. Given a proposition and a document, predict whether the proposition can be entailed/contradicted, or neutral with respect to the document. |
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``` |
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{ |
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"input": "[M]The Departed is[/M] a 2006 feature film [M]directed by Martin Scorsese.[/M][SEP]The Departed is a 2006 American crime thriller film directed by Martin Scorsese and written by William Monahan. It starred Leonardo DiCaprio, Matt Damon, Jack Nicholson, and Mark Wahlberg, with Martin Sheen, Ray Winstone, Vera Farmiga, and Alec Baldwin in supporting roles. It is a remake of the Hong Kong film Infernal Affairs (2002).\nThe Departed won the Oscar for Best Picture at the 79th Academy Awards. Scorsese received the Oscar for Best Director, Thelma Schoonmaker the Oscar for Best Editing and William Monahan the Oscar for Best Adapted Screenplay.", |
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"label": "e" |
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} |
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``` |
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### Citation |
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``` |
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@inproceedings{chen2023propsegment, |
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title = "{PropSegmEnt}: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition", |
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author = "Chen, Sihao and Buthpitiya, Senaka and Fabrikant, Alex and Roth, Dan and Schuster, Tal", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", |
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year = "2023", |
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} |
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``` |
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