Update description

README.md

@@ -100,13 +100,17 @@ where a triplet consists of (target, opinion, sentiment).
 Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint.
 The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service).
 By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.
-Datasets consisting of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect will be provided.
+This dataset consists of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect.
 
 ### Dataset Source
 
 The ASTE dataset is from the [xuuuluuu/SemEval-Triplet-data](https://github.com/xuuuluuu/SemEval-Triplet-data) repository.
 
-It is based on the [Sem Eval 2014 Task 4](https://alt.qcri.org/semeval2014/task4/) dataset, with some preprocessing applied to the text.
+It is based on the Sem Eval 2014, 2015 and 2016 datasets, with some preprocessing applied to the text.
+
+ * [Sem Eval 2014 Task 4](https://alt.qcri.org/semeval2014/task4/)
+ * [Sem Eval 2015 Task 12](https://alt.qcri.org/semeval2015/task12/)
+ * [Sem Eval 2016 Task 5](https://alt.qcri.org/semeval2016/task5/)
 
 ### Dataset Details