asparius/TurHistQuAD
收藏资源简介：
# Turkish Historic Question Dataset This data is orinally from https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset ## BibTeX Citation If you use this dataset, please cite following paper: ``` @INPROCEEDINGS{9559013, author={Soygazi, Fatih and Çiftçi, Okan and Kök, Uğurcan and Cengiz, Soner}, booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)}, title={THQuAD: Turkish Historic Question Answering Dataset for Reading Comprehension}, year={2021}, volume={}, number={}, pages={215-220}, keywords={Computer science;Computational modeling;Neural networks;Knowledge discovery;Information retrieval;Natural language processing;History;question answering;information retrieval;natural language understanding;deep learning;contextualized word embeddings}, doi={10.1109/UBMK52708.2021.9559013}} ``` --- license: mit ---
# 土耳其历史问答数据集 本数据集源自 https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset ## BibTeX 引用格式 若使用本数据集，请引用如下论文： @INPROCEEDINGS{9559013, author={Soygazi, Fatih and Çiftçi, Okan and Kök, Uğurcan and Cengiz, Soner}, booktitle={2021年第6届国际计算机科学与工程学术会议（UBMK）}, title={THQuAD：面向阅读理解的土耳其语历史问答数据集}, year={2021}, volume={}, number={}, pages={215-220}, keywords={计算机科学;计算建模;神经网络;知识发现;信息检索;自然语言处理;历史学;问答任务;信息检索;自然语言理解;深度学习;上下文词嵌入}, doi={10.1109/UBMK52708.2021.9559013}} --- 许可证：MIT许可证 ---
Turkish Historic Question Dataset
数据集来源
- 原始数据来自：https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset
BibTeX 引用
- 如果使用此数据集，请引用以下论文：
@INPROCEEDINGS{9559013, author={Soygazi, Fatih and Çiftçi, Okan and Kök, Uğurcan and Cengiz, Soner}, booktitle={2021 6th International Conference on Computer Science and Engineering (UBMK)}, title={THQuAD: Turkish Historic Question Answering Dataset for Reading Comprehension}, year={2021}, volume={}, number={}, pages={215-220}, keywords={Computer science;Computational modeling;Neural networks;Knowledge discovery;Information retrieval;Natural language processing;History;question answering;information retrieval;natural language understanding;deep learning;contextualized word embeddings}, doi={10.1109/UBMK52708.2021.9559013}}
许可证
- 许可证：MIT
