Update README.md

README.md

@@ -49,7 +49,9 @@ language:
 - Paper: https://aclanthology.org/2022.findings-emnlp.499.pdf  
 - Repository: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS  
 - Point of Contact: [Fenia Christopoulou](mailto:efstathia.christopoulou@huawei.com), [Chenxi Whitehouse](mailto:chenxi.whitehouse@gmail.com)
-  
+
+## Model Description
+
 This model has been trained on the EntityCS corpus, an English corpus from Wikipedia with replaced entities in different languages.  
 The corpus can be found in [https://huggingface.co/huawei-noah/entity_cs](https://huggingface.co/huawei-noah/entity_cs), check the link for more details.
 To train models on the corpus, we first employ the conventional 80-10-10 MLM objective, where 15% of sentence subwords are considered as masking candidates. From those, we replace subwords
@@ -105,7 +107,7 @@ In the paper, we focused on entity-related tasks, such as NER, Word Sense Disamb
 
 Alternatively, it can be used directly (no fine-tuning) for probing tasks, i.e. predict missing words, such as [X-FACTR](https://aclanthology.org/2020.emnlp-main.479/).
 
-For results on each downstream task, please refer to the paper.
+For results on each downstream task, please refer to the [paper](https://aclanthology.org/2022.findings-emnlp.499.pdf).
 
 
 ## How to Get Started with the Model
@@ -114,7 +116,7 @@ Use the code below to get started with the model: https://github.com/huawei-noah
 
 ## Citation
 
-**BibTeX:**
+**BibTeX**
 
 ```html
 @inproceedings{whitehouse-etal-2022-entitycs,
@@ -132,8 +134,8 @@ Use the code below to get started with the model: https://github.com/huawei-noah
 }
 ```
 
-**APA:**
+**APA**
 
 ```html
-Whitehouse, C., Christopoulou, F., & Iacobacci, I. (2022). EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 6698–6714). Association for Computational Linguistics.
+Whitehouse, C., Christopoulou, F., & Iacobacci, I. (2022). EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. In Findings of the Association for Computational Linguistics: EMNLP 2022.
 ```