[Doc] Old wines new title, edit for grammar and style (#1027)

* Old wines new title, edit for grammar and style

new descriptive title here and an edit pass @aaronmarkham

* Update tutorials/models/4_old_wines/README.txt

tutorials/models/4_old_wines/README.txt

 .. _tutorials4-index:
 
 
-Old (new) wines in new bottle
-=============================
+Revisit classic models from a graph perspective
+====================================
 
 * **Capsule** `[paper] <https://arxiv.org/abs/1710.09829>`__ `[tutorial]
-  <4_old_wines/2_capsule.html>`__ `[code]
+  <4_old_wines/2_capsule.html>`__ `[PyTorch code]
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/capsule>`__:
-  this new computer vision model has two key ideas -- enhancing the feature
-  representation in a vector form (instead of a scalar) called *capsule*, and
+  This new computer vision model has two key ideas. First, enhancing the feature
+  representation in a vector form (instead of a scalar) called *capsule*. Second,
   replacing max-pooling with dynamic routing. The idea of dynamic routing is to
-  integrate a lower level capsule to one (or several) of a higher level one
-  with non-parametric message-passing. We show how the later can be nicely
+  integrate a lower level capsule to one or several higher level capsules
+  with non-parametric message-passing. A tutorial shows how the latter can be 
   implemented with DGL APIs.
 
 
 * **Transformer** `[paper] <https://arxiv.org/abs/1706.03762>`__ `[tutorial] <4_old_wines/7_transformer.html>`__ 
-  `[code] <https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer>`__ and **Universal Transformer** 
+  `[PyTorch code] <https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer>`__ and **Universal Transformer** 
   `[paper] <https://arxiv.org/abs/1807.03819>`__ `[tutorial] <4_old_wines/7_transformer.html>`__
-  `[code] <https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer/modules/act.py>`__:
-  these two models replace RNN with several layers of multi-head attention to
+  `[PyTorch code] <https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer/modules/act.py>`__:
+  These two models replace recurrent neural networks (RNNs) with several layers of multi-head attention to
   encode and discover structures among tokens of a sentence. These attention
-  mechanisms can similarly formulated as graph operations with
-  message-passing.
+  mechanisms are similarly formulated as graph operations with message-passing.