Datasets:
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. We downsampled the training and development set to 25% of the original, and recovered labels for the development set from the original FEVER dataset.
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 (Orlando et al., EMNLP 2021) and InVeRo (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, modifying the labels to the following schema:
{
"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 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.