Update README.md

README.md

@@ -34,7 +34,7 @@ pretty_name:
 
 - **Homepage:** [https://patentdataset.org/](https://patentdataset.org/)
 - **Repository:** [HUPD GitHub repository](https://github.com/suzgunmirac/hupd)
-- **Paper:** [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](TBD)
+- **Paper:** [HUPD arXiv Submission](https://arxiv.org/abs/2207.04043)
 - **Point of Contact:** Mirac Suzgun
 
 ### Dataset Summary
@@ -143,7 +143,7 @@ dataset_dict = load_dataset('HUPD/hupd',
 
 #### Full Dataset
 
-If you would like to use the **full** version of the dataset, please make sure that change the `name` field from `sample` to `all`, specify the training and validation start and end dates carefilly, and set `force_extract` to be `True` (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.
+If you would like to use the **full** version of the dataset, please make sure that change the `name` field from `sample` to `all`, specify the training and validation start and end dates carefully, and set `force_extract` to be `True` (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.
 
 ```python
 from datasets import load_dataset
@@ -189,26 +189,28 @@ The dataset contains information about the inventor(s) and examiner of each pate
 
 ### Social Impact of the Dataset
 
-The authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in [the paper](TBD).
+The authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in [the paper](https://arxiv.org/abs/2207.04043).
 
 ### Impact on Underserved Communities and Discussion of Biases
 The dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.
 
 ### Discussion of Biases
-Section 4 of [the HUPD paper](TBD) provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.
+Section 4 of [the HUPD paper](https://arxiv.org/abs/2207.04043) provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.
 
 - Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.  
 
 
 ### Licensing Information
 
-HUPD is released under the Creative Commons Attribution 4.0 International License. 
+HUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International.
 
 ### Citation Information
 
 ```
 @article{suzgun2022hupd,
-  title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
-  author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K and Kominers, Scott and Shieber, Stuart},
-  year={2022}
+    title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
+    author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K. and Kominers, Scott Duke and Shieber, Stuart M.},
+    year={2022},
+    publisher={arXiv preprint arXiv:2207.04043},
+    url={https://arxiv.org/abs/2207.04043},
 ```