Machine Comprehension using match-LSTM and Answer-Pointer

By Akhil M. Nair, Ramkishan Panthena. 2017

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In Natural Language Processing

Citations in the corpus

SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016, background): “For example, Wang and Jiang (2016) obtained 70.3% F1 on SQuAD v1.”
Hierarchical Graph Network for Multi-hop Question Answering (2019, methodology): “Specifically, WikiHop is constructed using the schema of the underlying knowledge bases, thus limiting answers to entities only.”
Reinforced Mnemonic Reader for Machine Comprehension (2017, methodology): “…and Courville 2016) and large-scale MC benchmark datasets (Hermann et al. 2015; Hill et al. 2016; Rajpurkar et al. 2016; Joshi et al. 2017), end-to-end neural networks have achieved promising results on MC tasks (Wang et al. 2016; Xiong, Zhong, and Socher 2017; Seo et al. 2017; Wang et al. 2017).”
Scene Text Visual Question Answering (2019, background): “The recent advent of large scale datasets like the Stanford Question Answering Dataset (SQuAD) [42] and Microsoft Machine Reading Comprehension dataset (MS MACRO) [38] have propitiated the appearance of deep neural network models [17, 44, 51] that are able to automatically answer questions about a g”
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (2018, result): “RNN-based models : Previous best performing models are based on RNNs, we choose representative models as baselines, including SMNdynamic(Wu et al., 2017), Multiview(Zhou et al., 2016), DualEncoderlstm and DualEncoderbilstm (Lowe et al., 2015), DL2R (Yan et al., 2016), Match-LSTM (Wang and Jiang, 201”
Description Based Text Classification with Reinforcement Learning (2020, methodology): “Answering MRC models (Rajpurkar et al., 2016; Seo et al., 2016; Wang et al., 2016; Wang and Jiang, 2016; Xiong et al., 2016, 2017; Wang et al., 2016; Shen et al., 2017; Chen et al., 2017a; Rajpurkar et al., 2018) extract answer spans from passages given questions.”