Update README.md

README.md

@@ -56,7 +56,7 @@ task_ids:
 ## Dataset Description
 
 - **Repository:** [Github](https://github.com/avi-jit/numeracy-literacy)
-- **Paper:** [PDF](https://github.com/avi-jit/numeracy-literacy/blob/main/camera_ready.pdf)
+- **Paper:** [Anthology](https://aclanthology.org/2021.emnlp-main.557)
 - **Point of Contact:** [Avijit Thawani](mailto:thawani@isi.edu)
 
 ### Dataset Summary
@@ -75,7 +75,7 @@ The dataset is extracted from English Wikipedia, hence overwhelmingly contains E
 
 ### Data Instances
 
-Each row in the jsonline file contains metadata about the source Wikipedia sentence, along with annotations for a single number, e.g., `number: 10` in the below example. The annotations are inspired by Numeracy-600K and are in the form of `length` and `offset` from the beginning of the sentence.
+Each row in the json file contains metadata about the source Wikipedia sentence, along with annotations for a single number, e.g., `number: 10` in the below example. The annotations are inspired by Numeracy-600K and are in the form of `length` and `offset` from the beginning of the sentence.
 
 ```
 {
@@ -85,122 +85,34 @@ Each row in the jsonline file contains metadata about the source Wikipedia sente
 
 ### Data Fields
 
-List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
-
-- `id`: 
-- `UNIQUE_STORY_INDEX`:
-- `offset`: 83
-'length': 2, 'magnitude': 0, 'comment': "Like all Type UB III submarines, UB-117 carried 10 torpedoes and was armed with a  10 cms deck gun. ''", 'number': 10
-
-Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
+Please refer to https://github.com/avi-jit/numeracy-literacy for more details.
 
 ### Data Splits
 
-Describe and name the splits in the dataset if there are more than one.
-
-Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
-
-Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length.  For example:
-
 |                            | Tain   | Dev | Test |
-| -----                      | ------ | ----- | ---- |
+| -----                      | :------: | :-----: | :----: |
 | Input Sentences            |  739,583 | 92,447 | 92,449|
 
-## Dataset Creation
-
-### Curation Rationale
-
-What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
-
-### Source Data
-
-This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
-
-#### Initial Data Collection and Normalization
-
-Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
-
-If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
-
-If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
-
-#### Who are the source language producers?
-
-State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
-
-If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
-
-Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
-
-Describe other people represented or mentioned in the data. Where possible, link to references for the information.
-
-### Annotations
-
-If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
-
-#### Annotation process
-
-If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
-
-#### Who are the annotators?
-
-If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
-
-Describe the people or systems who originally created the annotations and their selection criteria if applicable.
-
-If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
-
-Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
-
-### Personal and Sensitive Information
-
-State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
-
-State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).  
-
-If efforts were made to anonymize the data, describe the anonymization process.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-Please discuss some of the ways you believe the use of this dataset will impact society.
-
-The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
-
-Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
-
-### Discussion of Biases
-
-Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
-
-For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
-
-If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
-
-### Other Known Limitations
-
-Wiki-Convert only has annotations for measured quantities and not other forms of numbers like ordinals or nominals.
-
-## Additional Information
-
-### Dataset Curators
-
-Extracted and cleaned from Wikipedia at the Information Sciences Institute, University of Southern California. Funded partially by the DARPA program on Machine Commonsense.
-
 ### Licensing Information
 
 Provided under MIT License.
 
 ### Citation Information
 
-Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
 ```
-@article{numeracy_literacy,
-  author    = {Avijit Thawani, Jay Pujara, Filip Ilievski},
-  title     = {Numeracy enhances Literacy in Language Models},
-  journal   = {The 2021 Conference on Empirical Methods in Natural Language Processing},
-  year      = {2021}
+@inproceedings{thawani-etal-2021-numeracy,
+    title = "Numeracy enhances the Literacy of Language Models",
+    author = "Thawani, Avijit  and
+      Pujara, Jay  and
+      Ilievski, Filip",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.557",
+    pages = "6960--6967",
+    abstract = "Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your {`}room{'} but not 500. Does a better grasp of numbers improve a model{'}s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.",
 }
 ```