Update multi-task learning example (apache#12964)

* Update multi task learning example

* Updating README.md

example/multi-task/README.md

@@ -1,10 +1,13 @@
 # Mulit-task learning example
 
-This is a simple example to show how to use mxnet for multi-task learning. It uses MNIST as an example and mocks up the multi-label task.
+This is a simple example to show how to use mxnet for multi-task learning. It uses MNIST as an example, trying to predict jointly the digit and whether this digit is odd or even.
 
-## Usage
-First, you need to write a multi-task iterator on your own. The iterator needs to generate multiple labels according to your applications, and the label names should be specified in the `provide_label` function, which needs to be consist with the names of output layers. 
+For example:
 
-Then, if you want to show metrics of different tasks separately, you need to write your own metric class and specify the `num` parameter. In the `update` function of metric, calculate the metrics separately for different tasks.
+![](https://camo.githubusercontent.com/ed3cf256f47713335dc288f32f9b0b60bf1028b7/68747470733a2f2f7777772e636c61737365732e63732e756368696361676f2e6564752f617263686976652f323031332f737072696e672f31323330302d312f70612f7061312f64696769742e706e67)
 
-The example script uses gpu as device by default, if gpu is not available for your environment, you can change `device` to be `mx.cpu()`.
+Should be jointly classified as 4, and Even.
+
+In this example we don't expect the tasks to contribute to each other much, but for example multi-task learning has been successfully applied to the domain of image captioning. In [A Multi-task Learning Approach for Image Captioning](https://www.ijcai.org/proceedings/2018/0168.pdf) by Wei Zhao, Benyou Wang, Jianbo Ye, Min Yang, Zhou Zhao, Ruotian Luo, Yu Qiao, they train a network to jointly classify images and generate text captions
+
+Please refer to the notebook for a fully worked example.