End-to-End Reading Comprehension with Dynamic Answer Chunk Ranking

By Yang Yu, Kazi Saidul Hasan, Mo Yu, Bing Xiang, Bowen Zhou. 2016

33 citations recorded by the source corpus.

Subfields: Retrieval-Augmented Generation, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Retrieval-Augmented Generation

1. Nan Yang
2. Debajyoti Chatterjee
3. Richard Socher
4. Caiming Xiong
5. Victor Zhong
6. Furu Wei
7. Wenhui Wang

In Natural Language Processing

1. Caiming Xiong
2. Furu Wei
3. Richard Socher
4. Mohit Bansal
5. Jianfeng Gao
6. Xipeng Qiu
7. Bryan McCann
8. M. Zhou

Citations in the corpus

• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension (2018, neutral): “…Published 12 LeaderBoard 13 Single Model EM / F1 EM / F1 LR Baseline (Rajpurkar et al., 2016) 40.4 / 51.0 40.4 / 51.0 Dynamic Chunk Reader (Yu et al., 2016) 62.5 / 71.0 62.5 / 71.0 Match-LSTM with Ans-Ptr (Wang & Jiang, 2016) 64.7 / 73.7 64.7 / 73.7 Multi-Perspective Matching (Wang et…”
• Bidirectional Attention Flow for Machine Comprehension (2016, neutral): “, 2016)(1), (Wang & Jiang, 2016)(2) and (Yu et al., 2016)(3) on the SQuAD test set.”
• The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “, 2017), question answering (Seo et al., 2017; Xiong et al., 2018; Yu et al., 2016; Weissenborn et al., 2017), and curriculum learning (Bengio et al.”
• Gated Self-Matching Networks for Reading Comprehension and Question Answering (2017, background): “Yu et al. (2016) and Lee et al. (2016) solve SQuAD by ranking continuous text spans within passage.”
• Dynamic Coattention Networks For Question Answering (2016, methodology): “…model that consists of a Match-LSTM encoder, originally introduced in Wang & Jiang (2016a), and a pointer network decoder (Vinyals et al., 2015); Yu et al. (2016) introduced a dynamic chunk reader, a neural reading comprehension model that extracts a set of answer candidates of variable lengths…”
• ReasoNet: Learning to Stop Reading in Machine Comprehension (2016, neutral): “…for this task is as follows: 5 SQuAD Competition Website is https://rajpurkar.github.io/SQuAD-explorer/ [18] 40.4 51.0 - - Dynamic Chunk Reader [34] 62.5 71.0 - - Fine-Grained Gating [33] 62.5 73.3 - - Match-LSTM [26] 64.7 73.7 67.9 77.0 RaSoR [14] - - 67.4 75.5 Multi-Perspective Matching [28]…”
• Making Neural QA as Simple as Possible but not Simpler (2017, background): “Another interesting finding is the fact that additional character based embeddings have a notable effect on the overall performance which was already observed by Seo et al. (2017); Yu et al. (2017).”
• S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension (2017, background): “Other works which only focus on the SQuAD dataset may also be applied on the MS-MARCO dataset (Yu et al., 2016; Lee et al., 2016; Yang et al., 2016).”
• Making Neural Machine Reading Comprehension Faster (2019, methodology): “ationally faster neural architecture which performs reasonably well on SQuAD task. 2 Chapter 2 Literature Review Most of the papers that approach to solve SQuAD problem have utilized RNN based models [3, 5, 7, 9, 13, 14, 16, 17, 20, 22{24, 26, 29{31, 34{39, 41, 43] and they follow a similar chain of”