Update README.md

README.md

@@ -41,7 +41,7 @@ license: cc-by-4.0
 
 ### Dataset Summary
 
-The CA-EN Parallel Corpus is a Catalan-English dataset of **14.385.296** parallel sentences. The dataset was created to support Catalan in NLP tasks, specifically 
+The CA-EN Parallel Corpus is a Catalan-English dataset of **14.967.979** parallel sentences. The dataset was created to support Catalan in NLP tasks, specifically 
 Machine Translation.
 
 ### Supported Tasks and Leaderboards
@@ -57,28 +57,38 @@ The sentences included in the dataset are in Catalan (CA) and English (EN).
 
 ### Data Instances
 
-The dataset is a single tsv file where each row contains a parallel sentence pair and additional domain and text type information for each sentence.
-Datafields are separated by \,
-Text delimiter is \"
+The dataset is a single tsv file where each row contains a parallel sentence pair, as well as the following information per sentence: 
+
+* language probability score calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py), 
+* alignment score calculated with [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), 
+* domain
+* text type.
 
 
 ### Data Fields
 
 Each example contains the following 7 fields: 
-* sentence_id: unique alphanumeric sentence identifier
-* en: ENGLISH
-* en_sentence: English sentence
-* ca: CATALAN
-* ca_sentence: Catalan sentence
-* domain: sentence domain
-* text_type: sentence text type
+
+* ca: Catalan sentence
+* en: English sentence
+* ca_prob: Language probability for the Catalan sentence calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py)
+* en_prob: Language probability for the English sentence calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py)
+* alignment: Sentence pair alignment score calculated with [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)
+* Domain: Domain (see List of domains)
+* Type: Text type (see list of text types)
 
 #### Example:
 
 <pre>
 [
   {
-    "00000a47-e4a5-8ab6-e0fa-3cbbeb596f34","en","As for the search engines, they also rely on the structure of your information content on the website to analyze and index your website.","ca","Pel que fa als motors de cerca, també es basen en l'estructura del seu contingut d'informació al lloc web per analitzar i indexar el seu lloc web.","MWM","SM"
+    Pel que fa als motors de cerca, també es basen en l'estructura del seu contingut d'informació al lloc web per analitzar i indexar el seu lloc web.
+    As for the search engines, they also rely on the structure of your information content on the website to analyze and index your website.
+    0.9999799355804416
+    0.9993718600460302
+    0.91045034
+    MWM
+    SM
 
   },
   ...
@@ -88,32 +98,52 @@ Each example contains the following 7 fields:
 
 List of domains
 
-* AUT: Automotive, transport, traffic regulations
-* LEG: legal, law, HR, certificates, degrees
-* MWM: Marketing, web, merchandising, customer support and service, e-commerce , advertising, surveys
-* LSM: Medicine, natural sciences, food/nutrition, biology, sexology, cosmetics, chemistry, genetics
-* ENV: Environment, agriculture, forestry, fisheries, farming, zoology, ecology
-* FIN: Finance, economics, business, entrepreneurship, business, competitions, labour, employment, accounting, insurance, insurance
-* POL: Politics, international relations, European Union, international organisations, defence, military
-* PRN: Porn, inappropriate content
-* COM: Computers, IT, robotics, domotics, home automation, telecommunications
-* ING: Pure engineering (mechanical, electrical, electronic, aerospace...), meteorology, mining, engineering, maritime, acoustics
-* ARC: Architecture, civil engineering, construction, public engineering
-* MAT: Mathematics, statistics, physics
-* HRM: History, religion, mythology, folklore, philosophy, psychology, ethics, anthropology, tourism
-* CUL: Art, poetry, literature, cinema, video games, theatre, theatre/film scripts, esotericism, astrology, sports, music, photography 
-* GEN: General - generic cathegory with topics such as clothing, textiles, gastronomy,  etc.
+AUT: Automotive, transport, traffic regulations
+
+LEG: legal, law, HR, certificates, degrees
+
+MWM: Marketing, web, merchandising, customer support and service, e-commerce , advertising, surveys
+
+LSM: Medicine, natural sciences, food/nutrition, biology, sexology, cosmetics, chemistry, genetics
+
+ENV: Environment, agriculture, forestry, fisheries, farming, zoology, ecology
+
+FIN: Finance, economics, business, entrepreneurship, business, competitions, labour, employment, accounting, insurance, insurance
+
+POL: Politics, international relations, European Union, international organisations, defence, military
+
+PRN: Porn, inappropriate content
+
+COM: Computers, IT, robotics, domotics, home automation, telecommunications
+
+ING: Pure engineering (mechanical, electrical, electronic, aerospace...), meteorology, mining, engineering, maritime, acoustics
+
+ARC: Architecture, civil engineering, construction, public engineering
+
+MAT: Mathematics, statistics, physics
+
+HRM: History, religion, mythology, folklore, philosophy, psychology, ethics, anthropology, tourism
+
+CUL: Art, poetry, literature, cinema, video games, theatre, theatre/film scripts, esotericism, astrology, sports, music, photography 
+
+GEN: General - generic cathegory with topics such as clothing, textiles, gastronomy,  etc.
 
 
 List of text types
 
-* PAT: Patents
-* SM: Social Media (social networks, chats, forums, tweets...)
-* CON: Vernacular (transcription of conversations, subtitles)
-* EML: Emails
-* MNL: Manuals, data sheets 
-* NEW: News, journalism
-* GEN: Prose, generic type of text
+PAT: Patents
+
+SM: Social Media (social networks, chats, forums, tweets...)
+
+CON: Vernacular (transcription of conversations, subtitles)
+
+EML: Emails
+
+MNL: Manuals, data sheets 
+
+NEW: News, journalism
+
+GEN: Prose, generic type of text
 
 
 ### Data Splits
@@ -135,10 +165,8 @@ domains and styles.
 The source data is Catalan authentic text translated to English or authentic English text translated to Catalan.
 
 The data was obtained through a combination of human translation and machine translation with human proofreading.
-After the translation process, the data was deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75 in order 
-to improve the data alignment quality.
-This was done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
-The obtained cleaned corpus consists of **14.385.296** parallel sentences of human quality.
+
+The obtained corpus consists of **14.967.979** parallel sentences of human quality.
 
 #### Who are the source language producers?
 
@@ -171,7 +199,8 @@ Inherent biases may exist within the data.
 
 ### Other Known Limitations
 
-The dataset contains data of several specific domains. Application of this dataset in other domains would be of limited use.
+The dataset contains data of several specific domains. The dataset can be used as a whole or extracting subsets per domain or text types. 
+Applications of this dataset in domains other than the ones included in the domain list would be of limited use.
 
 ## Additional Information