MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)
Copied to clipboard
Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
Similar Papers
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation (P18-1)
Copied to clipboard
Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev
| Challenge: | TutorialBank is a publicly available dataset that aims to facilitate NLP education and research . a google search of "Natural Language Processing" returns over 100 million hits with papers, tutorials, 1 http://aan.how blog posts, codebases and other related online resources. |
| Approach: | They have manually collected and categorized over 5,600 resources on NLP . they have created a search engine and command-line tool to search the corpus . |
| Outcome: | The tutorial bank dataset is the largest manually-picked corpus of resources intended for NLP education . it includes lists of research topics, relevant resources for each topic, prerequisite relations among topics . |
NLP Scholar: A Dataset for Examining the State of NLP Research (2020.lrec-1)
Copied to clipboard
| Challenge: | Google Scholar is the largest web search engine for academic literature and provides access to rich metadata associated with the papers. |
| Approach: | They extracted citation information from the ACL Anthology (AA) for about 44 thousand NLP papers and identified authors who published at least three papers there. |
| Outcome: | The ACL Anthology (AA) is the largest repository of articles on Natural Language Processing (NLP). |
Datasets: A Community Library for Natural Language Processing (2021.emnlp-demo)
Copied to clipboard
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, Thomas Wolf
| Challenge: | Contemporary NLP systems use many different datasets at significantly varying scale and level of annotation. |
| Approach: | a community library for contemporary NLP is available at https://github.com/datasets . the library includes more than 650 unique datasets and has more than 250 contributors a year after its initial development . |
| Outcome: | the library includes more than 650 unique datasets and has more than 250 contributors . it supports a variety of cross-dataset research projects and shared tasks . |
DocNLI: A Large-scale Dataset for Document-level Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems. |
| Approach: | They propose to construct a large-scale dataset for document-level NLI that can be used to study NLP problems. |
| Outcome: | The proposed model performs well on popular sentence-level benchmarks and generalizes well to out-of-domain NLP tasks that rely on inference at document granularity. |
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
Copied to clipboard
| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
| Outcome: | The first workshop on crowdsourcing for NLP received 16 submissions and accepted 7 . the workshop will focus on ambiguous, subjective or ambiguity analysis of crowdsourced data . |
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
Copied to clipboard
| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)
Copied to clipboard
Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang
| Challenge: | Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios. |
| Approach: | They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts. |
| Outcome: | The proposed model performs comparable to state-of-the-art large models on the test set. |
Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages (2024.lrec-main)
Copied to clipboard
Daan van Esch, Sandy Ritchie, Sebastian Ruder, Julia Kreutzer, Clara Rivera, Ishank Saxena, Isaac Caswell
| Challenge: | Existing data sources for many thousands of languages are rich and diverse . Efforts are ongoing to extend technology to many more of the world's languages . |
| Approach: | They provide an overview of some of the major online data sources available for thousands of languages. |
| Outcome: | The proposed language technologies are based on the data available for thousands of languages. |
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (2021.eacl-main)
Copied to clipboard
| Challenge: | Open Information Extraction (OIE) systems extract factual propositions into n-ary tuples . current datasets are limited in size and diversity . |
| Approach: | They propose to convert QA-SRL 2.0 dataset to large-scale OIE dataset LSOIE. |
| Outcome: | The proposed dataset is 20 times larger than the next largest human-annotated OIE dataset. |
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
Copied to clipboard
| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |