Datasets:
tommasobonomo
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README.md
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path: data/validation-*
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- split: test
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path: data/test-*
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---
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path: data/validation-*
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- split: test
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path: data/test-*
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license: mit
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language:
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- en
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pretty_name: Semantically-augmented FEVER for NLI
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size_categories:
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- 10K<n<100K
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---
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# Semantically-augmented FEVER for NLI
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This dataset is a random downsample of the [FEVER dataset adapted for NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md).
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We downsampled the training and development set to 25% of the original, and recovered labels for the development set from the original [FEVER dataset](https://huggingface.co/datasets/fever/fever).
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The dataset is also augmented with semantic annotations such as Word Sense Disambiguation (WSD) and Semantic Role Labeling (SRL) information.
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We annotated the whole downsampled dataset (both `premise` and `hypothesis`) with [AMuSE-WSD](https://aclanthology.org/2021.emnlp-demo.34) (Orlando et al., EMNLP 2021) and [InVeRo](https://aclanthology.org/2020.emnlp-demos.11) (Conia et al., EMNLP 2020).
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## Dataset Creation
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The idea was to curate a version of the FEVER dataset adapted to the NLI task for Homework 2 of the Multilingual Natural Language Processing 2024 course at Sapienza University of Rome.
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We sourced the data following the instructions in [this repo](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), modifying the labels to the following schema:
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```json
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{
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"id": ..., # the FEVER dataset ID
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"premise": ..., # the context in FEVER
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"hypothesis": ..., # the query in FEVER
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"label": ..., # mapped version of FEVER, where 'supports' -> 'entailment', 'refutes' -> 'contradiction' and 'not enough info' -> 'neutral'
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}
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```
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We filtered out any sample with an empty premise (a minority of the data) and downsampled both `train_fitems.jsonl` and `dev_fitems.jsonl` to 25% of their total size.
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We then recovered the labels for the development set from the `labelled_dev` split in the [original FEVER](https://huggingface.co/datasets/fever/fever) and split the development set into the final `dev.jsonl` and `test.jsonl`.
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Finally, we ran both AMuSE-WSD and InVeRo to augment our samples with WSD and SRL annotations, respectively.
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