Question Answering
125 items from seriousran/awesome-qa ★770
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BERT github.com
A new language representation model which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in…
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DeepMind Q&A Dataset; CNN/Daily Mail github.com
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BiDAF github.com
Bi-Directional Attention Flow (BIDAF) network is a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.
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QANet github.com
A Q&A architecture does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions.
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R-Net github.com
An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage.
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NarrativeQA github.com
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DrQA github.com
DrQA is a system for reading comprehension applied to open-domain question answering.
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ELI5 github.com
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NewsQA github.com
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GraphQuestions github.com
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SQuAD1.0 rajpurkar.github.io
Question answering dataset that can be explored online, and a list of models performing well on that dataset.
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https://github.com/xwhan/ProQA github.com
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CODAH Dataset github.com
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https://github.com/wissam-sib/dilbert github.com
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Question Answering - Natural Language Processing youtube.com
By Dragomir Radev, Ph.D. | University of Michigan | 2016.
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https://unifiedqa.apps.allenai.org/ unifiedqa.apps.allenai.org
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Children's Book Test uclmr.github.io
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Building a Question-Answering System from Scratch— Part 1 towardsdatascience.com
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LC-QuAD sda.cs.uni-bonn.de
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"A survey on question answering technology from an information retrieval perspective" sciencedirect.com
, Information Sciences, 2011.
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Neural Compositional Denotational Semantics for Question Answering research.fb.com
, Nitish Gupta, Mike Lewis, EMNLP, 2018
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Embodied Question Answering research.fb.com
, Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra, CVPR, 2018
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Do explanations make VQA models more predictable to a human? research.fb.com
, Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh, EMNLP, 2018
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Facebook DrQA research.fb.com
Applied to the SQuAD1.0 dataset. The SQuAD2.0 dataset has released. but DrQA is not tested yet.
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TinyBERT: Distilling BERT for Natural Language Understanding openreview.net
, Xiaoqi Jiao, et al., ICLR, 2020.
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators openreview.net
, Kevin Clark, et al., ICLR, 2020.
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