RACE数据集是多项选择阅读理解(Multi Choice MRC)中的经典数据集,RACE官方网址[5] 中的介绍为: Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle ...
[8] Generating multiple-choice questions for medical question answering with distractors and cue-masking 标题:利用干扰因子和线索掩蔽生成医学问题回答的选择题 链接:arxiv.org/abs/2303.0706 代码:未开源 [9] An Interactive UI to Support Sensemaking over Collections of Parallel Texts 标题:支持对平行文本集...
训练过程中采用的损失函数如下: 这个模型还可以转化为上述的cloze,multiple-choice等类型的MRC任务,做一些简单的调整即可。 我们前面还介绍过,如何基于BERT来做MRC的任务,感兴趣的读者可以看看: 【NLP】如何利用BERT来做基于阅读理解的信息抽取 总结 基于MRC可以完成知识抽取、QA等重要的NLP任务,读者务必熟悉。
Learning to ask good questions: Ranking clarification questions using neural expected value of perfect...
Semantic Graphs for Generating Deep Questions. Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan. ACL 2020 [pdf] [code] Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation. Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che , Ting Liu...
(SQuAD 2.0)Know What You Don't Know: Unanswerable Questions for SQuAD.Pranav Rajpurkar, Robin Jia, and Percy Liang. ACL 2018. (MS MARCO)MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and ...
Multiple-Choice Question Answering (QA):The task of QA is to answer a given question by selecting one of the multiple choices. Questions are often accompanied by supporting facts which contain further context. Selecting the correct option out of all choices can be considered as a sequential decis...
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations (CIKM2019) Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering What does BERT Learn from Multiple-Choice Reading Comprehension Datasets? Calibration of Pre-trained Transformers ...
Objective of the code was to build DL model(s) to answer 8th grade multiple-choice science questions, provided as part of this AllenAI competition on Kaggle. Models Much of the inspiration for the DL implementations in this project came from the solution posted by the 4th place winner of th...
These tasks include boolean question answering, where the answer is either yes or no, causal reasoning or reading comprehension with multiple-choice questions. As of today, the best performing model on SuperGLUE is T5 from Google, published in October 2019, but it still ranks under the SuperGLUE...