Updated as far as social impact

README.md

@@ -221,6 +221,11 @@ dataset_info:
     num_examples: 118296182
   download_size: 16568412828
   dataset_size: 21749592609
+license: other
+task_categories:
+- text-classification
+size_categories:
+- 100M<n<1B
 ---
 # Dataset Card for "open-lid-dataset"
 
@@ -243,7 +248,46 @@ There are 201 languages included in the dataset with varying amounts of data: th
 
 ## Dataset Structure
 
+### Data Instances
+
 Each entry in the dataset consists of a line of data, a language label included script information, and a tag indicating the source.
 ```json
-{}
+{
+  "text": "¿Serás exaltada hasta el cielo?",
+  "language": "spa_Latn",
+  "dataset_source": "lti" 
+}
 ```
+
+### Data Splits
+
+Only a train split is provided. The dataset is designed to be compatible with the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) evaluation benchmark.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Recent work has found that existing language identification algorithms perform poorly in practice compared to test performance. The problem is particularly acute for low-resource languages: [Kreutzer et al. (2022)](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00447/109285/Quality-at-a-Glance-An-Audit-of-Web-Crawled) found a positive Spearman rank correlation between quality of data and size of language for all of the \ac{lid}-filtered multilingual datasets they studied. In addition, for a significant fraction of the language corpora they studied, less than half of the sentences were in the correct language. They point out that such low-quality data not only leads to poor performance in downstream tasks, but that it also contributes to `representation washing', where the community is given a false view of the actual progress of low-resource natural language processing.
+
+There are several open language identification models offering quick classification and high language coverage (e.g. CLD3, No Language Left Behind). However, to the best of our knowledge, none of the commonly-used scalable language identificaiton systems make their training data public. 
+
+This dataset aims to address that gap by curating and combining sources of open training data for language identification and by auditing a sample of all languages in each source to check reliability.
+
+### Source Data
+
+The majority of the source datasets were derived from news sites, Wikipedia, or religious text, though some come from other domains (e.g. transcribed conversations, literature, or social media). We provide a full list at the end of this model card along with the licensing information for each source.
+
+#### Initial Data Collection and Normalisation
+
+Our initial aim was to cover the same languages present in the FLORES-200 Evaluation Benchmark so that we could use this dataset for evaluation. However, during the curation process, we decided to exclude three languages. Firstly, though Akan and Twi are both included as separate languages in FLORES-200, Akan is actually a macrolanguage covering a language continuum which includes Twi. Given the other languages in FLORES-200 are individual languages, we decided to exclude Akan. Secondly, FLORES-200 includes Modern Standard Arabic (MSA) written in Latin script. It is true that Arabic dialects are often written in Latin characters in informal situations (e.g. social media). However, MSA is a form of standardised Arabic which is not usually used in informal situations. Since we could not any find naturally-occurring training data, we excluded MSA from the dataset. Finally, we excluded Minangkabau in Arabic script because it is now rarely written this way, making it difficult to find useful training data.
+
+The first step in our manual audit was to check and standardise language labels, as these are often inconsistent or idiosyncratic. We chose to copy the language codes in FLORES-200 and reassign macrolanguage or ambiguous language codes in the data sources we found to the dominant individual language. Whilst this resulted in more useful data for some languages, for other languages we had to be more conservative. For example, we originally reassigned text labelled as the macrolanguage Malay (msa_Latn) to Standard Malay, but this led to a large drop in performance as the former covers a very diverse set of languages.
+
+
+Two of the authors then carried out a manual audit of a random sample of all data sources and languages: one a native Bulgarian speaker (able to read Cyrillic and Latin scripts and Chinese characters), and the other a native English speaker (able to read Latin, Arabic and Hebrew scripts). For languages we knew, we checked the language was what we expected. For unfamiliar languages in a script we could read, we compared the sample to the Universal Declaration of Human Rights or failing that, to a sample of text on Wikipedia. We compared features of the text which are common in previous language identification algorithms and could be identified easily by humans: similar diacritics, word lengths, common words, loan words matching the right cultural background, similar suffixes and prefixes, and vowel/consonant patterns. For scripts we could not read, we checked that all lines of the sample matched the script in the Universal Declaration of Human Rights.
+
+We kept preprocessing minimal so that the process was as language agnostic as possible. We used the scripts provided with Moses to remove non-printing characters and detokenise the data where necessary. We then filtered the data so that each line contained at least one character in the expected script (as defined by Perl) to allow for borrowings. Finally, we sampled proportionally to $ p_l^{0.3} $, where $ p_l $ is the fraction of lines in the dataset which are in language $ l $. This aims to ameliorate class skew issues.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset