Update README.md

README.md

@@ -10,7 +10,7 @@ size_categories:
 
 <!-- Provide a quick summary of the dataset. -->
 
-This dataset has been proposed as one of the Challenge of the special event "CALAMITA2024", co-located with the Tenth Italian Conference on Computational Linguistics (https://clic2024.ilc.cnr.it/calamita/).
+This dataset has been proposed as part of the CALAMITA Challenge, the special event co-located with the Tenth Italian Conference on Computational Linguistics (https://clic2024.ilc.cnr.it/calamita/).
 
 The dataset focuses on evaluating Language Models' understanding of a specific linguistic structure in Italian: object-extracted relative clauses (ORCs). 
 The assessment involves a yes/no entailment task in which the model is given two sentences. The first contains the target structure, and the second is a simple declarative sentence whose meaning may or may not be inferred from the first, based on the syntactic relationship between the elements in the ORC. 
@@ -22,7 +22,7 @@ The assessment involves a yes/no entailment task in which the model is given two
 
 <!-- Provide a longer summary of what this dataset is. -->
 
-The dataset contains 566 pairs of sentences, where the first sentence includes the ORC and the second sentence is the declarative sentence which can be implied or not by the first.
+The benchmark contains 566 pairs of sentences, where the first sentence includes the ORC and the second sentence is the declarative sentence which can be implied or not by the first.
 The ORCs have been primarily sourced from linguistic and psycholinguistic literature to explore the impact of grammatical and semantic features on the processing difficulties humans face when reading ORCs.
 
 A smaller portion of the dataset includes ORCs from existing NLP benchmarks, which are specifically designed to test language models' capabilities in recognizing grammaticality.