Update README.md

README.md

@@ -23,14 +23,14 @@ For quick start please have a look this [demo](https://github.com/ahmedssabir/Te
           (3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong 
           relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then 
           we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage 
-          of the visual overlap  between caption and visual context, and to extract global information, we use BERT followed by a shallow CNN (<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
+          of the visual overlap  between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)
           to estimate the visual relatedness score. 
  
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 ## Dataset 
-
+ (<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
 ### Sample 
  
  ```