Further Updated README

README.md

@@ -62,26 +62,46 @@ ficle_data = load_dataset("tathagataraha/ficle")
 
 ## Dataset Description
 
-* **GitHub Repo:** 
+* **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 Instances
-
 ### Data Fields
 
-* `content`: 
+* `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 rows
@@ -92,12 +112,34 @@ The dataset is split into `train`, `validation`, and `test`.
 
 ### Curation Rationale
 
-### Source Data
+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](https://github.com/blitzprecision/FICLE/blob/main/ficle_annotation_guidelines.pdf).
+
+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.
+
+
 ### Personal and Sensitive Information
 
 ## Considerations for Using the Data