Update README.md

README.md

@@ -14,118 +14,112 @@ pretty_name: dynamically generated hate speech dataset
 
 ## Dataset Description
 
-- **Homepage:** 
-- **Repository:** 
-- **Paper:** 
-- **Leaderboard:** 
-- **Point of Contact:** 
+- **Homepage:** [GitHub](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset)
+- **Point of Contact:** [[email protected]](mailto:[email protected])
 
 ### Dataset Summary
 
 This is a copy of the Dynamically-Generated-Hate-Speech-Dataset, presented in [this paper](https://arxiv.org/abs/2012.15761) by
-- **Bertie Vidgen**
-- **Tristan Thrush**
-- **Zeerak Waseem**
-- **and Douwe Kiela**
+- **Bertie Vidgen**, **Tristan Thrush**, **Zeerak Waseem** and **Douwe Kiela**
 
 This repository only contains v0.2.3, I have not many any additional changes to this paper.
 
 ## Original README from [GitHub](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset/blob/main/README.md)
 
-# Dynamically-Generated-Hate-Speech-Dataset
-ReadMe for v0.2 of the Dynamically Generated Hate Speech Dataset from Vidgen et al. (2021). If you use the dataset, please cite our paper in the Proceedings of ACL 2021, and available on [Arxiv](https://arxiv.org/abs/2012.15761).
-Contact Dr. Bertie Vidgen if you have feedback or queries: [email protected].
-
-The full author list is: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook AI Research), Zeerak Waseem (University of Sheffield) and Douwe Kiela (Facebook AI Research). This paper is an output of the Dynabench project: https://dynabench.org/tasks/5#overall
-
-## Dataset descriptions
-v0.2.2.csv is the full dataset used in our ACL paper. 
-
-v0.2.3.csv removes duplicate entries, all of which occurred in round 1. Duplicates come from two sources: (1) annotators entering the same content multiple times and (2) different annotators entering the same content. The duplicates are interesting for understanding the annotation process, and the challenges of dynamically generating datasets. However, they are likely to be less useful for training classifiers and so are removed in v0.2.3. We did not lower case the text before removing duplicates as capitalisations contain potentially useful signals.
-
-
-## Overview
-The Dynamically Generated Hate Speech Dataset is provided in one table.
-
-'acl.id' is the unique ID of the entry.
-
-'Text' is the content which has been entered. All content is synthetic.
-
-'Label' is a binary variable, indicating whether or not the content has been identified as hateful. It takes two values: hate, nothate.
-
-'Type' is a categorical variable, providing a secondary label for hateful content. For hate it can take five values: Animosity, Derogation, Dehumanization, Threatening and Support for Hateful Entities. Please see the paper for more detail. For nothate the 'type' is 'none'. In round 1 the 'type' was not given and is marked as 'notgiven'.
-
-'Target' is a categorical variable, providing the group that is attacked by the hate. It can include intersectional characteristics and multiple groups can be identified. For nothate the type is 'none'. Note that in round 1 the 'target' was not given and is marked as 'notgiven'.
-
-'Level' reports whether the entry is original content or a perturbation.
-
-'Round' is a categorical variable. It gives the round of data entry (1, 2, 3 or 4) with a letter for whether the entry is original content ('a') or a perturbation ('b'). Perturbations were not made for round 1.
-
-'Round.base' is a categorical variable. It gives the round of data entry, indicated with just a number (1, 2, 3 or 4).
-
-'Split' is a categorical variable. it gives the data split that the entry has been assigned to. This can take the values 'train', 'dev' and 'test'. The choice of splits is explained in the paper.
-
-'Annotator' is a categorical variable. It gives the annotator who entered the content. Annotator IDs are random alphanumeric strings. There are 20 annotators in the dataset.
-
-'acl.id.matched' is the ID of the matched entry, connecting the original (given in 'acl.id') and the perturbed version.
-
-
-For identities (recorded under 'Target') we use shorthand labels to constructed the dataset, which can be converted (and grouped) as follows:
-
-	none -> for non hateful entries 
-	NoTargetRecorded -> for hateful entries with no target recorded
-	
-	mixed -> Mixed race background
-	ethnic minority -> Ethnic Minorities
-	indig -> Indigenous people
-	indigwom -> Indigenous Women
-	non-white -> Non-whites (attacked as 'non-whites', rather than specific non-white groups which are generally addressed separately)
-	trav -> Travellers (including Roma, gypsies)
-
-	bla -> Black people
-	blawom -> Black women
-	blaman -> Black men
-	african -> African (all 'African' attacks will also be an attack against Black people)
-	
-	jew -> Jewish people
-	mus -> Muslims
-	muswom -> Muslim women
-
-	wom -> Women	
-	trans -> Trans people
-	gendermin -> Gender minorities, 
-	bis -> Bisexual
-	gay -> Gay people (both men and women)
-	gayman -> Gay men
-	gaywom -> Lesbians	
-	
-	dis -> People with disabilities
-	working -> Working class people
-	old -> Elderly people
-
-	asi -> Asians
-	asiwom -> Asian women
-	east -> East Asians
-	south -> South Asians (e.g. Indians)
-	chinese -> Chinese people
-	pak -> Pakistanis
-	arab -> Arabs, including people from the Middle East
-
-	immig -> Immigrants
-	asylum -> Asylum seekers
-	ref -> Refguees
-	for -> Foreigners
-	
-	eastern european -> Eastern Europeans
-	russian -> Russian people
-	pol -> Polish people
-	hispanic -> Hispanic people, including latinx and Mexicans
-
-	nazi -> Nazis ('Support' type of hate)
-	hitler -> Hitler ('Support' type of hate)
-	
-## Code
-Code was implemented using hugging face transformers library.
+  # Dynamically-Generated-Hate-Speech-Dataset
+  ReadMe for v0.2 of the Dynamically Generated Hate Speech Dataset from Vidgen et al. (2021). If you use the dataset, please cite our paper in the Proceedings of ACL 2021, and available on [Arxiv](https://arxiv.org/abs/2012.15761).
+  Contact Dr. Bertie Vidgen if you have feedback or queries: [email protected].
+  
+  The full author list is: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook AI Research), Zeerak Waseem (University of Sheffield) and Douwe Kiela (Facebook AI Research). This paper is an output of the Dynabench project: https://dynabench.org/tasks/5#overall
+  
+  ## Dataset descriptions
+  v0.2.2.csv is the full dataset used in our ACL paper. 
+  
+  v0.2.3.csv removes duplicate entries, all of which occurred in round 1. Duplicates come from two sources: (1) annotators entering the same content multiple times and (2) different annotators entering the same content. The duplicates are interesting for understanding the annotation process, and the challenges of dynamically generating datasets. However, they are likely to be less useful for training classifiers and so are removed in v0.2.3. We did not lower case the text before removing duplicates as capitalisations contain potentially useful signals.
+  
+  
+  ## Overview
+  The Dynamically Generated Hate Speech Dataset is provided in one table.
+  
+  'acl.id' is the unique ID of the entry.
+  
+  'Text' is the content which has been entered. All content is synthetic.
+  
+  'Label' is a binary variable, indicating whether or not the content has been identified as hateful. It takes two values: hate, nothate.
+  
+  'Type' is a categorical variable, providing a secondary label for hateful content. For hate it can take five values: Animosity, Derogation, Dehumanization, Threatening and Support for Hateful Entities. Please see the paper for more detail. For nothate the 'type' is 'none'. In round 1 the 'type' was not given and is marked as 'notgiven'.
+  
+  'Target' is a categorical variable, providing the group that is attacked by the hate. It can include intersectional characteristics and multiple groups can be identified. For nothate the type is 'none'. Note that in round 1 the 'target' was not given and is marked as 'notgiven'.
+  
+  'Level' reports whether the entry is original content or a perturbation.
+  
+  'Round' is a categorical variable. It gives the round of data entry (1, 2, 3 or 4) with a letter for whether the entry is original content ('a') or a perturbation ('b'). Perturbations were not made for round 1.
+  
+  'Round.base' is a categorical variable. It gives the round of data entry, indicated with just a number (1, 2, 3 or 4).
+  
+  'Split' is a categorical variable. it gives the data split that the entry has been assigned to. This can take the values 'train', 'dev' and 'test'. The choice of splits is explained in the paper.
+  
+  'Annotator' is a categorical variable. It gives the annotator who entered the content. Annotator IDs are random alphanumeric strings. There are 20 annotators in the dataset.
+  
+  'acl.id.matched' is the ID of the matched entry, connecting the original (given in 'acl.id') and the perturbed version.
+  
+  
+  For identities (recorded under 'Target') we use shorthand labels to constructed the dataset, which can be converted (and grouped) as follows:
+  
+  	none -> for non hateful entries 
+  	NoTargetRecorded -> for hateful entries with no target recorded
+  	
+  	mixed -> Mixed race background
+  	ethnic minority -> Ethnic Minorities
+  	indig -> Indigenous people
+  	indigwom -> Indigenous Women
+  	non-white -> Non-whites (attacked as 'non-whites', rather than specific non-white groups which are generally addressed separately)
+  	trav -> Travellers (including Roma, gypsies)
+  
+  	bla -> Black people
+  	blawom -> Black women
+  	blaman -> Black men
+  	african -> African (all 'African' attacks will also be an attack against Black people)
+  	
+  	jew -> Jewish people
+  	mus -> Muslims
+  	muswom -> Muslim women
+  
+  	wom -> Women	
+  	trans -> Trans people
+  	gendermin -> Gender minorities, 
+  	bis -> Bisexual
+  	gay -> Gay people (both men and women)
+  	gayman -> Gay men
+  	gaywom -> Lesbians	
+  	
+  	dis -> People with disabilities
+  	working -> Working class people
+  	old -> Elderly people
+  
+  	asi -> Asians
+  	asiwom -> Asian women
+  	east -> East Asians
+  	south -> South Asians (e.g. Indians)
+  	chinese -> Chinese people
+  	pak -> Pakistanis
+  	arab -> Arabs, including people from the Middle East
+  
+  	immig -> Immigrants
+  	asylum -> Asylum seekers
+  	ref -> Refguees
+  	for -> Foreigners
+  	
+  	eastern european -> Eastern Europeans
+  	russian -> Russian people
+  	pol -> Polish people
+  	hispanic -> Hispanic people, including latinx and Mexicans
+  
+  	nazi -> Nazis ('Support' type of hate)
+  	hitler -> Hitler ('Support' type of hate)
+  	
+  ## Code
+  Code was implemented using hugging face transformers library.
 
 ## Additional Information