dataset_info:
features:
- name: Claim
dtype: string
- name: Context
dtype: string
- name: Source
dtype: string
- name: Source Indices
dtype: string
- name: Relation
dtype: string
- name: Relation Indices
dtype: string
- name: Target
dtype: string
- name: Target Indices
dtype: string
- name: Inconsistent Claim Component
dtype: string
- name: Inconsistent Context-Span
dtype: string
- name: Inconsistent Context-Span Indices
dtype: string
- name: Inconsistency Type
dtype: string
- name: Fine-grained Inconsistent Entity-Type
dtype: string
- name: Coarse Inconsistent Entity-Type
dtype: string
splits:
- name: train
num_bytes: 2657091
num_examples: 6443
- name: validation
num_bytes: 333142
num_examples: 806
- name: test
num_bytes: 332484
num_examples: 806
download_size: 1784422
dataset_size: 3322717
task_categories:
- token-classification
language:
- en
pretty_name: FICLE Dataset
size_categories:
- 1K<n<10K
FICLE Dataset
The dataset can be loaded and utilized through the following:
from datasets import load_dataset
ficle_data = load_dataset("tathagataraha/ficle")
Dataset card for Falcon RefinedWeb
Dataset Description
- GitHub Repo: https://github.com/blitzprecision/FICLE
- Paper:
- Point of Contact:
Dataset Summary
The FICLE dataset is a derivative of the FEVER dataset, which is a collection of 185,445 claims generated by modifying sentences obtained from Wikipedia. These claims were then verified without knowledge of the original sentences they were derived from. Each sample in the FEVER dataset consists of a claim sentence, a context sentence extracted from a Wikipedia URL as evidence, and a type label indicating whether the claim is supported, refuted, or lacks sufficient information.
Languages
The FICLE Dataset contains only English.
Dataset Structure
Data Fields
Claim (string):Context (string):Source (string):Source Indices (string):Relation (string):Relation Indices (string):Target (string):Target Indices (string):Inconsistent Claim Component (string):Inconsistent Context-Span (string):Inconsistent Context-Span Indices (string):Inconsistency Type (string):Fine-grained Inconsistent Entity-Type (string):Coarse Inconsistent Entity-Type (string):
Data Splits
The FICLE dataset comprises a total of 8,055 samples in the English language, each representing different instances of inconsistencies. These inconsistencies are categorized into five types: Taxonomic Relations (4,842 samples), Negation (1,630 samples), Set Based (642 samples), Gradable (526 samples), and Simple (415 samples).
Within the dataset, there are six possible components that contribute to the inconsistencies found in the claim sentences. These components are distributed as follows: Target-Head (3,960 samples), Target-Modifier (1,529 samples), Relation-Head (951 samples), Relation-Modifier (1,534 samples), Source-Head (45 samples), and Source-Modifier (36 samples).
The dataset is split into train, validation, and test.
train: 6.44k rowsvalidation: 806 rowstest: 806 rows
Dataset Creation
Curation Rationale
We propose a linguistically enriched dataset to help detect inconsistencies and explain them. To this end, the broad requirements are to locate where the inconsistency is present between a claim and a context and to have a classification scheme for better explainability.
Data Collection and Preprocessing
The FICLE dataset is derived from the FEVER dataset, using the following- ing processing steps. FEVER (Fact Extraction and VERification) consists of 185,445 claims were generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. Every sample in the FEVER dataset contains the claim sentence, evidence (or context) sentence from a Wikipedia URL, and a type label (‘supports’, ‘refutes’, or ‘not enough info’). Out of these, we leverage only the samples with the ‘refutes’ label to build our dataset.
Annotations
You can see the annotation guidelines here.
In order to provide detailed explanations for inconsistencies, extensive annotations were conducted for each sample in the FICLE dataset. The annotation process involved two iterations, with each iteration focusing on different aspects of the dataset. In the first iteration, the annotations were primarily "syntactic-oriented." These fields included identifying the inconsistent claim fact triple, marking inconsistent context spans, and categorizing the six possible inconsistent claim components. The second iteration of annotations concentrated on "semantic-oriented" aspects. Annotators labeled semantic fields for each sample, such as the type of inconsistency, coarse inconsistent entity types, and fine-grained inconsistent entity types. This stage aimed to capture the semantic nuances and provide a deeper understanding of the inconsistencies present in the dataset.
The annotation process was carried out by a group of four annotators, two of whom are also authors of the dataset. The annotators possess a strong command of the English language and hold Bachelor's degrees in Computer Science, specializing in computational linguistics. Their expertise in the field ensured accurate and reliable annotations. The annotators' ages range from 20 to 22 years, indicating their familiarity with contemporary language usage and computational linguistic concepts.