QASPER: NLP Questions and Evidence
Discovering Answers with Expertise
By Huggingface Hub [source]
About this dataset
QASPER is an incredible collection of over 5,000 questions and answers on a vast range of Natural Language Processing (NLP) papers -- all crowdsourced from experienced NLP practitioners. Each question in the dataset is written based only on the titles and abstracts of the corresponding paper, providing an insight into how the experts understood and parsed various materials. The answers to each query have been expertly enriched by evidence taken directly from the full text of each paper. Moreover, QASPER comes with carefully crafted fields that contain relevant information including ‘qas’ – questions and answers; ‘evidence’ – evidence provided for answering questions; title; abstract; figures_and_tables, and full_text. All this adds up to create a remarkable dataset for researchers looking to gain insights into how practitioners interpret NLP topics while providing effective validation when it comes to finding clear-cut solutions to problems encountered in existing literature
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How to use the dataset
This guide will provide instructions on how to use the QASPER dataset of Natural Language Processing (NLP) questions and evidence. The QASPER dataset contains 5,049 questions over 1,585 papers that has been crowdsourced by NLP practitioners. To get the most out of this dataset we will show you how to access the questions and evidence, as well as provide tips for getting started.
Step 1: Accessing the Dataset
To access the data you can download it from Kaggle's website or through a code version control system like Github. Once downloaded, you will find five files in .csv format; two test data sets (test.csv and validation.csv), two train data sets (train-v2-0_lessons_only_.csv and trainv2-0_unsplit.csv) as well as one figure data set (figures_and_tables_.json). Each .csv file contains different datasets with columns representing titles, abstracts, full texts and Q&A fields with evidence for each paper mentioned in each row of each file respectively
**Step 2: Analyzing Your Data Sets **
Now would be a good time to explore your datasets using basic descriptive statistics or more advanced predictive analytics such as logistic regression or naive bayes models depending on what kind of analysis you would like to undertake with this dataset You can start simple by summarizing some basic crosstabs between any two variables comprise your dataset; titles abstracts etc.). As an example try correlating title lengths with certain number of words in their corresponding abstracts then check if there is anything worth investigating further
**Step 3: Define Your Research Questions & Perform Further Analysis **
Once satisfied with your initial exploration it is time to dig deeper into the underlying QR relationship among different variables comprising your main documents One way would be using text mining technologies such as topic modeling machine learning techniques or even automated processes that may help summarize any underlying patterns Yet another approach could involve filtering terms that are relevant per specific research hypothesis then process such terms via web crawlers search engines document similarity algorithms etc
Finally once all relevant parameters are defined analyzed performed searched it would make sense to draw preliminary connsusison linking them back together before conducting replicable tests ensuring reproducible results
Research Ideas
- Developing AI models to automatically generate questions and answers from paper titles and abstracts.
- Enhancing machine learning algorithms by combining the answers with the evidence provided in the dataset to find relationships between papers.
- Creating online forums for NLP practitioners that uses questions from this dataset to spark discussion within the community
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: validation.csv
| Column name |
Description |
| title |
The title of the paper. (String) |
| abstract |
A summary of the paper. (String) |
| full_text |
The full text of the paper. (String) |
| qas |
Questions and answers about the paper. (Object) |
| figures_and_tables |
Figures and tables from the paper. (Object) |
File: train.csv
| Column name |
Description |
| title |
The title of the paper. (String) |
| abstract |
A summary of the paper. (String) |
| full_text |
The full text of the paper. (String) |
| qas |
Questions and answers about the paper. (Object) |
| figures_and_tables |
Figures and tables from the paper. (Object) |
File: test.csv
| Column name |
Description |
| title |
The title of the paper. (String) |
| abstract |
A summary of the paper. (String) |
| full_text |
The full text of the paper. (String) |
| qas |
Questions and answers about the paper. (Object) |
| figures_and_tables |
Figures and tables from the paper. (Object) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.