Fetching the paper…
Reading the bibliography…
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects.
Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi. 2016 · 2016
Earlier work this paper cites.
Learning to use formulas to solve simple arithmetic problems
Arindam Mitra and Chitta Baral. 2016 · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang. 2016 · 2016
Earlier work this paper cites.
OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander Rush. 2017 · 2017
Earlier work this paper cites.
Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017a · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017b · 2017
Earlier work this paper cites.
Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
Flowqa: Grasping flow in history for conversational machine comprehension
Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih. 2018 · 2018
Earlier work this paper cites.
How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
A meaning-based statistical English math word problem solver
Chao-Chun Liang, Yu-Shiang Wong, Yi-Chung Lin, and Keh-Yih Su. 2018 · 2018
Cited alongside, same era.
Mapping to declarative knowledge for word problem solving
Subhro Roy and Dan Roth. 2018 · 2018
Cited alongside, same era.
The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
Cited alongside, same era.
Translating a math word problem to a expression tree
Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, and Xiaojiang Liu. 2018a · 2018
Cited alongside, same era.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Dream: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019 · 2019
Later among the works it cites.
Template-based math word problem solvers with recursive neural networks
Lei Wang, Dongxiang Zhang, Zhang Jipeng, Xing Xu, Lianli Gao, Bing Tian Dai, and Heng Tao Shen. 2019 · 2019
Later among the works it cites.
Hybridqa: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020 · 2020
Later among the works it cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Later among the works it cites.
Measuring systematic generalization in neural proof generation with transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy, and Chris Pal. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018 · 2018
Cited alongside, same era.
MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion
Philipp Christmann, Rishiraj Saha Roy, Abdalghani Abujabal, Jyotsna Singh, and Gerhard Weikum. 2019 · 2019
Cited alongside, same era.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020
Later among the works it cites.
R4c: A benchmark for evaluating rc systems to get the right answer for the right reason
Naoya Inoue, Pontus Stenetorp, and Kentaro Inui. 2020 · 2020
Later among the works it cites.
A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
Later among the works it cites.
Semantically-aligned universal tree-structured solver for math word problems
Jinghui Qin, Lihui Lin, Xiaodan Liang, Rumin Zhang, and Liang Lin. 2020 · 2020
Later among the works it cites.
PRover: Proof generation for interpretable reasoning over rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal. 2020 · 2020
Later among the works it cites.
Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020 · 2020
Later among the works it cites.
A knowledge-aware sequence-to-tree network for math word problem solving
Qinzhuo Wu, Qi Zhang, Jinlan Fu, and Xuanjing Huang. 2020 · 2020
Later among the works it cites.
Graph-to-tree learning for solving math word problems
Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, and Ee-Peng Lim. 2020 · 2020
Later among the works it cites.
Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
Closest in time.
Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
Closest in time.