4、TILE: Neural Generative Question Answering Author:Jun Yin • Xin Jiang • Zhengdong Lu Paper:https://arxiv.org/pdf/1512.01337v4.pdf Code:https://github.com/jxfeb/Generative_QA 论文简述:本文介绍了一种端到端神经网络模型,称为神经生成问答(GENQA),该模型可以基于知识库中的事实生成简单事实...
4、TILE: A Question-Focused Multi-Factor Attention Network for Question Answering Author: Souvik Kundu , Hwee Tou Ng Paper: arxiv.org/pdf/1801.0829 Code: github.com/nusnlp/amand 论文简述: 本文提出了一种新颖的端到端以问题为中心的多因素注意力网络,用于答案提取。 使用基于张量的变换进行多...
Code: github.com/facebookrese 论文简述: 本文深入分析了在一系列最先进的预训练语言模型中已经存在(没有微调)的关系知识。我们发现:(1)在没有微调的情况下,BERT包含了与传统NLP方法相竞争的关系知识,后者可以访问oracle知识;(2)BERT在有监督基线的开放域问题回答上也做得非常好,(3)通过标准语言模型的预训练方...
DeepQA is a library for doing high-level NLP tasks with deep learning, particularly focused on various kinds of question answering. DeepQA is built on top of Keras and TensorFlow, and can be thought of as an interface to these systems that makes NLP easier. Specifically, this library provide...
ADNOC_NLP_QuestionAndAnswering_System This project presents an innovative NLP-based Q&A system tailored for drilling reports generated during the drilling process. Leveraging state-of-the-art natural language processing techniques, the system efficiently extracts valuable insights from drilling reports, enabl...
6、TILE: Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index Author:Minjoon Seo,Jinhyuk Lee,Tom Kwiatkowski,Ankur P. Parikh,Ali Farhadi,Hannaneh Hajishirzi Paper:https://arxiv.org/pdf/1906.05807v2.pdf Code:https://github.com/uwnlp/denspi ...
Arabic low-resource NLPRanking MRCWeak supervisionThis work tackles the challenge of ranking-based machine reading comprehension (MRC), where a question answering (QA) system generates a ranked list of relevant answers for each question instead of simply extracting a single answer. We highlight the...
14See the web page titled “GitHub - cslu-nlp/DetectorMorse: Fast supervised sentence boundary detection using the averaged perceptron” (https://github.com/cslu-nlp/detectormorse). spaCy[15] 15See the web page titled “Facts & Figures - spaCy Usage Documentation” (https://spacy.io/usage/...
论文解读:Question Answering over Freebase with Multi-Column Convolutional Neural Networks KB-QA是一种问答系统任务,其是基于知识库进行的问答。给定一个知识库,其包含若干个实体和边,每两个实体和相连的边为一个三元组。实体分为客观实体和属性,客观实体就是客观存在的一般实体,例如人名地名机构名,属性则...
论文解读: R3:Reinforced Ranker-Reader for Open-Domain Question Answering,论文解读::ReinforcedRanker-ReaderforOpen-DomainQuestionAnswering 开放领域问答主要目标是从开放的资源中寻找答案,在目前自动问答任务中十分关键。本文是一篇2017年AAAI会议的问