diff --git a/pages/topics/question/README.md b/pages/topics/question/README.md
index 93a1122a..651a2952 100644
--- a/pages/topics/question/README.md
+++ b/pages/topics/question/README.md
@@ -1,11 +1,9 @@
 # Question Answering
 
-[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation)
+[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) (private)
 
 ## Project Description
 
-Task definition:
-
 - Create a clone of [SQuaD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) in the Slovak language
 - Setup annotation infrastructure with [Prodigy](https://prodi.gy/)
 - Perform and evaluate annotations of [Wikipedia data](https://dumps.wikimedia.org/backup-index.html).
@@ -129,12 +127,13 @@ TBD
 - Reading Wikipedia to Answer Open-Domain Questions, Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
 Facebook Research
 - SQuAD: 100,000+ Questions for Machine Comprehension of Text https://arxiv.org/abs/1606.05250
+- [WDaqua](https://wdaqua.eu/our-work/) publications
 
 ## Existing Datasets
 
-- Squad TheStanfordQuestionAnsweringDataset(SQuAD)  (Rajpurkar  et  al.,  2016) 
-- WebQuestions
-- https://en.wikipedia.org/wiki/Freebase
+- [Squad](https://rajpurkar.github.io/SQuAD-explorer/)  The Stanford Question Answering Dataset(SQuAD)  (Rajpurkar  et  al.,  2016) 
+- [WebQuestions](https://github.com/brmson/dataset-factoid-webquestions)
+- [Freebase](https://en.wikipedia.org/wiki/Freebase)
 
 ## Intern tasks
 
@@ -142,6 +141,7 @@ Week 1: Intro
 
 - Get acquainted with the project and Squad Database
 - Download the database and study the bibliography
+- Study [Prodigy annnotation](https://Prodi.gy) tool
 
 Week 2 and 3: Web Application
 
@@ -160,7 +160,7 @@ Select and train a working question answering system
 Output:
 
 - a deployment script with comments for a selected question answering system 
-- a working training recipe (can use English ata), a script with comments or Jupyter Notebook
+- a working training recipe (can use English data), a script with comments or Jupyter Notebook
 - a trained model
 - evaluation of the model (if possible)