add arxiv link

README.md

@@ -1,6 +1,6 @@
 # ETM
 
-This is code that accompanies the paper titled "Topic Modeling in Embedding Spaces" by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. (Arxiv link: )
+This is code that accompanies the paper titled "Topic Modeling in Embedding Spaces" by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. (Arxiv link: https://arxiv.org/abs/1907.04907)
 
 ETM defines words and topics in the same embedding space. The likelihood of a word under ETM is a Categorical whose natural parameter is given by the dot product between the word embedding and its assigned topic's embedding. ETM is a document model that learns interpretable topics and word embeddings and is robust to large vocabularies that include rare words and stop words.
 
@@ -39,7 +39,7 @@ python main.py --mode train --dataset 20ng --data_path data/20ng --emb_path PATH
 @article{dieng2019topic,
   title={Topic modeling in embedding spaces},
   author={Dieng, Adji B and Ruiz, Francisco J R and Blei, David M},
-  journal={arXiv preprint arXiv:},
+  journal={arXiv preprint arXiv:1907.04907},
   year={2019}
 }
 ```