Update README.md

data/README.md

@@ -1,6 +1,6 @@
 # Data
 
-We used the [Blogs Authorship Corpus](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm), [IMDb62](https://umlt.infotech.monash.edu/?page_id=266), [Amazon 5-core Reviews](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/) datasets to construct our benchmark. The description of the how the data was selected, processed, and split into train, dev, and test splits is described in our [paper](). The data we used is available in this [release]().
+We used the [Blogs Authorship Corpus](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm), [IMDb62](https://umlt.infotech.monash.edu/?page_id=266), [Amazon 5-core Reviews](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/) datasets to construct our benchmark. The description of the how the data was selected, processed, and split into Train, Dev, and Test splits is described in our [paper](). The data we used is available in this [release]().
 
 ## Benchmark Construction:
 
@@ -9,4 +9,4 @@ Reproducing our benchmark can be obtained by using the `create_data.sh` script,
 1. Downloading each dataset separately and applying a preprocessing step if needed.
 2. Annotating the data examples with fine-grained linguistic features by using the `utils/annotate_data.py` script.
 3. Obtaining the RST relations by using [rstfinder](rstfinder). This can be done by 1) writing every data example to a separate file and 2) invoking the [rstfinder/parse_data.sh](rstfinder/parse_data.sh) script.
-4. Spliting and descritizing the data to create train, dev, and test splits using the [utils/split_and_discretize.py](utils/split_and_discretize.py) script.
+4. Spliting and descritizing the data to create Train, Dev, and Test splits using the [utils/split_and_discretize.py](utils/split_and_discretize.py) script.