Update README.md

README.md

@@ -2,29 +2,18 @@
 Automatically Extracting Challenge Sets for Non-local Phenomena in Neural Machine Translation git repo
 
 Please cite if you find it useful:
-
-@inproceedings{choshen-abend-2019-automatically,
-
-title = "Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation",
-
-author = "Choshen, Leshem  and
-  Abend, Omri",
-
-booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
-
-month = nov,
-
-year = "2019",
-
-address = "Hong Kong, China",
-
-publisher = "Association for Computational Linguistics",
-
-url = "https://www.aclweb.org/anthology/K19-1028",
-
-doi = "10.18653/v1/K19-1028",
-
-pages = "291--303",
-
-abstract = "We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We therefore propose an automatic approach for extracting challenge sets rich with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.",
-}
+  https://www.aclweb.org/anthology/K19-1028/
+  ```
+  @inproceedings{choshen-abend-2019-automatically,
+  title = "Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation",
+  author = "Choshen, Leshem  and  Abend, Omri",
+  booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
+  month = nov,
+  year = "2019",
+  address = "Hong Kong, China",
+  publisher = "Association for Computational Linguistics",
+  url = "https://www.aclweb.org/anthology/K19-1028",
+  doi = "10.18653/v1/K19-1028",
+  pages = "291--303",
+  abstract = "We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We therefore propose an automatic approach for extracting challenge sets rich with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.",
+  }```