Update README.md

README.md

@@ -65,13 +65,13 @@ Text delimiter is \"
 ### Data Fields
 
 Each example contains the following fields: 
-* sentence_id: 
-* en:
-* en_sentence:
-* ca:
-* ca_sentence:
-* domain:
-* text_type:
+* 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
 
 #### Example:
 
@@ -84,9 +84,37 @@ Each example contains the following fields:
   ...
 ]
 
+</pre>
 
+####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.
+
+
+####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
 
-</pre>
 
 ### Data Splits
 
@@ -102,10 +130,9 @@ This dataset is aimed at promoting the development of Machine Translation betwee
 
 #### Initial Data Collection and Normalization
 
-The The data is a brand new collection of parallel sentences in Catalan and English, partially derived from web crawlings and belonging to a mix of different 
+The data is a brand new collection of parallel sentences in Catalan and English, partially derived from web crawlings and belonging to a mix of different 
 domains and styles.
 The source data is Catalan authentic text translated to English or authentic English text translated to Catalan.
-The original data gathering was entrusted to an external company through a public tender process.
 
 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 
@@ -115,7 +142,7 @@ The obtained cleaned corpus consists of **14.385.296** parallel sentences of hum
 
 #### Who are the source language producers?
 
-[N/A]
+The original data gathering was entrusted to an external company through a public tender process.
 
 ### Annotations
 
@@ -144,7 +171,7 @@ Inherent biases may exist within the data.
 
 ### Other Known Limitations
 
-The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
+The dataset contains data of several specific domains. Application of this dataset in other domains would be of limited use.
 
 ## Additional Information