Update README.md

README.md

@@ -33,7 +33,7 @@ Supported datasets are:
 - MultiWOZ 2.4 (https://github.com/smartyfh/MultiWOZ2.4.git)
 - Unified data format (currently supported: MultiWOZ 2.1) (see https://github.com/ConvLab/ConvLab-3)
 
-See the README file in `data/` for more details how to obtain and prepare the datasets for use in TripPy.
+See the README file in 'data/' for more details how to obtain and prepare the datasets for use in TripPy.
 
 The ```--task_name``` is
 - 'sim-m', for sim-M
@@ -41,6 +41,7 @@ The ```--task_name``` is
 - 'woz2', for WOZ 2.0
 - 'multiwoz21', for MultiWOZ 2.0-2.4
 - 'multiwoz21_legacy', for MultiWOZ 2.1 legacy version
+- 'unified', for ConvLab-3's unified data format
 
 With a sequence length of 180, you should expect the following average JGA:
 - 53% for MultiWOZ 2.0
@@ -55,9 +56,9 @@ With a sequence length of 180, you should expect the following average JGA:
 
 ## ConvLab-3
 
-TripPy is integrated in ConvLab-3 as ready-to-use dialogue state tracker. A checkpoint is available at HuggingFace (see the ConvLab-3 repo for more details).
+TripPy is integrated in ConvLab-3 as ready-to-use dialogue state tracker. A checkpoint is available at HuggingFace (see the [ConvLab-3 repo](https://github.com/ConvLab/ConvLab-3) for more details).
 
-If you want to train your own TripPy model for ConvLab-3 from scratch, you can do so by using this code, setting ```--task_name='unified'```. The ```--data_dir``` parameter will be ignored in that case. Pick the file for ```--dataset_config``` according to the dataset you want to train for. For MultiWOZ, this would 'data/unified_multiwoz21'.
+If you want to train your own TripPy model for ConvLab-3 from scratch, you can do so by using this code, setting ```--task_name='unified'```. The ```--data_dir``` parameter will be ignored in that case. Pick the file for ```--dataset_config``` according to the dataset you want to train for. For MultiWOZ, this would 'data/unified_multiwoz21.json'.
 
 ## Requirements
 
@@ -85,7 +86,7 @@ If you use TripPy in your own work, please cite our work as follows:
 }
 ```
 
-This repository also contains the code of our paper [Out-of-Task Training for Dialog State Tracking Models"](https://www.aclweb.org/anthology/2020.coling-main.596).
+This repository also contains the code of our paper [Out-of-Task Training for Dialog State Tracking Models](https://www.aclweb.org/anthology/2020.coling-main.596).
 
 If you use TripPy for MTL, please cite our work as follows: