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---
language:
- en
license: mit
size_categories:
- 10K<n<100K
pretty_name: Semantically-augmented FEVER for NLI
dataset_info:
features:
- name: id
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
- name: wsd
struct:
- name: premise
list:
- name: index
dtype: int64
- name: text
dtype: string
- name: pos
dtype: string
- name: lemma
dtype: string
- name: bnSynsetId
dtype: string
- name: wnSynsetOffset
dtype: string
- name: nltkSynset
dtype: string
- name: hypothesis
list:
- name: index
dtype: int64
- name: text
dtype: string
- name: pos
dtype: string
- name: lemma
dtype: string
- name: bnSynsetId
dtype: string
- name: wnSynsetOffset
dtype: string
- name: nltkSynset
dtype: string
- name: srl
struct:
- name: premise
struct:
- name: tokens
list:
- name: index
dtype: int64
- name: rawText
dtype: string
- name: annotations
list:
- name: tokenIndex
dtype: int64
- name: verbatlas
struct:
- name: frameName
dtype: string
- name: roles
list:
- name: role
dtype: string
- name: score
dtype: float64
- name: span
sequence: int64
- name: englishPropbank
struct:
- name: frameName
dtype: string
- name: roles
list:
- name: role
dtype: string
- name: score
dtype: float64
- name: span
sequence: int64
- name: hypothesis
struct:
- name: tokens
list:
- name: index
dtype: int64
- name: rawText
dtype: string
- name: annotations
list:
- name: tokenIndex
dtype: int64
- name: verbatlas
struct:
- name: frameName
dtype: string
- name: roles
list:
- name: role
dtype: string
- name: score
dtype: float64
- name: span
sequence: int64
- name: englishPropbank
struct:
- name: frameName
dtype: string
- name: roles
list:
- name: role
dtype: string
- name: score
dtype: float64
- name: span
sequence: int64
splits:
- name: train
num_bytes: 357653267
num_examples: 51086
- name: validation
num_bytes: 15794078
num_examples: 2288
- name: test
num_bytes: 15736002
num_examples: 2287
download_size: 77623798
dataset_size: 389183347
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Semantically-augmented FEVER for NLI
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).
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).
The dataset is also augmented with semantic annotations such as Word Sense Disambiguation (WSD) and Semantic Role Labeling (SRL) information.
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).
## Dataset Creation
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.
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:
```json
{
"id": ..., # the FEVER dataset ID
"premise": ..., # the context in FEVER
"hypothesis": ..., # the query in FEVER
"label": ..., # mapped version of FEVER, where 'supports' -> 'entailment', 'refutes' -> 'contradiction' and 'not enough info' -> 'neutral'
}
```
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.
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`.
Finally, we ran both AMuSE-WSD and InVeRo to augment our samples with WSD and SRL annotations, respectively. |