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Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community.
Finbert: Financial sentiment analysis with pre-trained language models
Dogu Araci. 2019 · 1908
Earlier work this paper cites.
A framework for anomaly detection using language modeling, and its applications to finance
Armineh Nourbakhsh and Grace Bang. 2019 · 1908
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Www’18 open challenge: financial opinion mining and question answering
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018 · 1942
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
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Free-marginal multirater kappa (multirater k [free]): An alternative to fleiss’ fixed-marginal multirater kappa
Justus J Randolph. 2005 · 2005
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Finbert: A pretrained language model for financial communications
Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020 · 2006
Earlier work this paper cites.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Semantic parsing via staged query graph generation: Question answering with knowledge base
Wen-tau Yih, Ming-Wei Chang, Xiaodong He, and Jianfeng Gao. 2015 · 2015
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
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
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Leveraging news sentiment to improve microblog sentiment classification in the financial domain
Tobias Daudert, Paul Buitelaar, and Sapna Negi. 2018 · 2018
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NextGen AML: Distributed deep learning based language technologies to augment anti money laundering investigation
Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, and Dadong Wan. 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
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A simple end-to-end question answering model for product information
Tuan Lai, Trung Bui, Sheng Li, and Nedim Lipka. 2018 · 2018
Earlier work this paper cites.
Causality analysis of Twitter sentiments and stock market returns
Narges Tabari, Piyusha Biswas, Bhanu Praneeth, Armin Seyeditabari, Mirsad Hadzikadic, and Wlodek Zadrozny. 2018 · 2018
Earlier work this paper cites.
The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
Earlier work this paper cites.
Word embeddings-based uncertainty detection in financial disclosures
Christoph Kilian Theil, Sanja Štajner, and Heiner Stuckenschmidt. 2018 · 2018
Earlier work this paper cites.
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
Cited alongside, same era.
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 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.
Utilizing pre-trained word embeddings to learn classification lexicons with little supervision
Frederick Blumenthal and Ferdinand Graf. 2019 · 2019
Cited alongside, same era.
A time series analysis of emotional loading in central bank statements
Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
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Tapas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Mueller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
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Do language embeddings capture scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020 · 2020
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Sven Buechel, Simon Junker, Thore Schlaak, Claus Michelsen, and Udo Hahn. 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.
Financial event extraction using Wikipedia-based weak supervision
Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, and Noam Slonim. 2019 · 2019
Cited alongside, same era.
Complaint analysis and classification for economic and food safety
João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, and Ana Maria Oliveira. 2019 · 2019
Cited alongside, same era.
A multi-type multi-span network for reading comprehension that requires discrete reasoning
Minghao Hu, Yuxing Peng, Zhen Huang, and Dongsheng Li. 2019 · 2019
Cited alongside, same era.
Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA
Yichen Jiang and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, and William Yang Wang. 2021 · 2021
Later among the works it cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Learning by fixing: Solving math word problems with weak supervision
Yining Hong, Qing Li, Daniel Ciao, Siyuan Huang, and Song-Chun Zhu. 2021 · 2021
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Ait-qa: Question answering dataset over complex tables in the airline industry
Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, and Soumen Chakrabarti. 2021 · 2021
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Tsqa: Tabular scenario based question answering
Xiao Li, Yawei Sun, and Gong Cheng. 2021 · 2021
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Scigen: a dataset for reasoning-aware text generation from scientific tables
Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, and Iryna Gurevych. 2021 · 2021
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Towards table-to-text generation with numerical reasoning
Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura, and Hiroya Takamura. 2021 · 2021
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Multimodal{qa}: complex question answering over text, tables and images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
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Tuta: Tree-based transformers for generally structured table pre-training
Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang. 2021 · 2021
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NOAHQA: Numerical reasoning with interpretable graph question answering dataset
Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, and Ee-Peng Lim. 2021 · 2021
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Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. 2021 · 2021
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TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021 · 2021
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FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, and Dragomir Radev. 2022 · 2022
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I Wang, et al. 2022 · 2022
Closest in time.
Tableformer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022 · 2022
Closest in time.