[Doc] Generative models, edit for readability (#1033)

Edit pass for grammar and style

tutorials/models/3_generative_model/README.txt

@@ -4,18 +4,18 @@ Generative models
 ==================
 
 * **DGMG** `[paper] <https://arxiv.org/abs/1803.03324>`__ `[tutorial]
-  <3_generative_model/5_dgmg.html>`__ `[code]
+  <3_generative_model/5_dgmg.html>`__ `[PyTorch code]
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgmg>`__:
-  this model belongs to the important family that deals with structural
-  generation. DGMG is interesting because its state-machine approach is the
-  most general. It is also very challenging because, unlike Tree-LSTM, every
+  This model belongs to the family that deals with structural
+  generation. Deep generative models of graphs (DGMG) uses a state-machine approach. 
+  It is also very challenging because, unlike Tree-LSTM, every
   sample has a dynamic, probability-driven structure that is not available
-  before training. We are able to progressively leverage intra- and
+  before training. You can progressively leverage intra- and
   inter-graph parallelism to steadily improve the performance.
 
-* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[code]
+* **JTNN** `[paper] <https://arxiv.org/abs/1802.04364>`__ `[PyTorch code]
   <https://github.com/dmlc/dgl/tree/master/examples/pytorch/jtnn>`__:
-  unlike DGMG, this paper generates molecular graphs using the framework of
-  variational auto-encoder. Perhaps more interesting is its approach to build
-  structure hierarchically, in the case of molecular, with junction tree as
+  This network generates molecular graphs using the framework of
+  a variational auto-encoder. The junction tree neural network (JTNN) builds
+  structure hierarchically. In the case of molecular graphs, it uses a junction tree as
   the middle scaffolding.