Update files from the datasets library (from 1.4.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.4.0

README.md

@@ -49,27 +49,27 @@ task_ids:
 
 - **Homepage:** https://sites.google.com/view/sdu-aaai21/shared-task
 - **Repository:** https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI
-- **Paper:** https://arxiv.org/pdf/2010.14678v1.pdf
+- **Paper:** [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf)
 - **Leaderboard:** https://competitions.codalab.org/competitions/26609
 - **Point of Contact:** [More Information Needed]
 
 ### Dataset Summary
 
-[More Information Needed]
+This dataset contains the training, validation, and test data for the **Shared Task 1: Acronym Identification** of the AAAI-21 Workshop on Scientific Document Understanding.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609).
 
 ### Languages
 
-[More Information Needed]
+The sentences in the dataset are in English (`en`).
 
 ## Dataset Structure
 
 ### Data Instances
 
-A sample training set is provided below
+A sample from the training set is provided below:
 
 ```
 {'id': 'TR-0',
@@ -94,50 +94,62 @@ A sample training set is provided below
   '.']}
 ```
 
-Please note that in test set sentence only id, tokens are available. labels can be ignored for test set.
-Labels in the test set are all `O`
+Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O`
 
 ### Data Fields
 
-[More Information Needed]
+The data instances have the following fields:
+
+- `id`: a `string` variable representing the example id, unique across the full dataset
+- `tokens`: a list of `string` variables representing the word-tokenized sentence
+- `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as *controlled natural language* here, and `-short` to the abbrviation, `CNL` here.
 
 ### Data Splits
 
-[More Information Needed]
+The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+> First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods.
+> This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text.
+> Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains.
+> In order to address these limitations this paper introduces two new datasets for Acronym Identification.
+> Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain. 
 
 ### Source Data
 
-[More Information Needed]
-
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+> In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv.
+> These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work.
+
+The dataset paper does not report the exact tokenization method.
 
 #### Who are the source language producers?
 
-[More Information Needed]
+The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers.
 
 ### Annotations
 
-[More Information Needed]
-
 #### Annotation process
 
-[More Information Needed]
+> Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates).
+> Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate.
+> We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence.
+> Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk).
+> In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence.
+> In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation.
+> Otherwise, a fourth annotator is hired to resolve the conflict
 
 #### Who are the annotators?
 
-[More Information Needed]
+Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided.
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care.
 
 ## Considerations for Using the Data
 
@@ -161,11 +173,31 @@ Labels in the test set are all `O`
 
 ### Licensing Information
 
-[More Information Needed]
+The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license.
 
 ### Citation Information
 
-[More Information Needed]
+```
+@inproceedings{Veyseh2020,
+  author    = {Amir Pouran Ben Veyseh and
+               Franck Dernoncourt and
+               Quan Hung Tran and
+               Thien Huu Nguyen},
+  editor    = {Donia Scott and
+               N{\'{u}}ria Bel and
+               Chengqing Zong},
+  title     = {What Does This Acronym Mean? Introducing a New Dataset for Acronym
+               Identification and Disambiguation},
+  booktitle = {Proceedings of the 28th International Conference on Computational
+               Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13,
+               2020},
+  pages     = {3285--3301},
+  publisher = {International Committee on Computational Linguistics},
+  year      = {2020},
+  url       = {https://doi.org/10.18653/v1/2020.coling-main.292},
+  doi       = {10.18653/v1/2020.coling-main.292}
+}
+```
 
 ### Contributions