2021

FinQA: A Dataset of Numerical Reasoning over Financial Data

Chen, Zhiyu, Chen, Wenhu, Smiley, Charese et al.

Understand

The sheer volume of financial statements makes it difficult for humans to access and analyze a business's financials.

Robust numerical reasoning likewise faces unique challenges in this domain.
In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents.
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.

Built on

A BERT baseline for the natural questions
Original
Chris Alberti, Kenton Lee, and Michael Collins. 2019 · 1901
Earlier work this paper cites.
Roberta: A robustly optimized BERT pretraining approach
Original
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.
Finbert: Financial sentiment analysis with pre-trained language models
Original
Dogu Araci. 2019 · 1908
Earlier work this paper cites.
A framework for anomaly detection using language modeling, and its applications to finance
Original
Armineh Nourbakhsh and Grace Bang. 2019 · 1908
Earlier work this paper cites.
Longformer: The long-document transformer
Original
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
Finbert: A pretrained language model for financial communications
Original
Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020 · 2006
Earlier work this paper cites.
An engine, not a camera: How financial models shape markets
Donald MacKenzie. 2008 · 2008
Earlier work this paper cites.
Drilling through the allegheny mountains: Liquidity, materiality and high-frequency trading
Donald MacKenzie, Daniel Beunza, Yuval Millo, and Juan Pablo Pardo-Guerra. 2012 · 2012
Earlier work this paper cites.
Poor numbers: how we are misled by African development statistics and what to do about it
Morten Jerven. 2013 · 2013
Earlier work this paper cites.
Financial sentiment analysis for risk prediction
Chuan-Ju Wang, Ming-Feng Tsai, Tse Liu, and Chin-Ting Chang. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.

Similar

Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
Cited alongside, same era.
Deep learning for financial sentiment analysis on finance news providers
Min-Yuh Day and Chia-Chou Lee. 2016 · 2016
Cited alongside, same era.
MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi. 2016 · 2016
Cited alongside, same era.
Cultures of high-frequency trading: Mapping the landscape of algorithmic developments in contemporary financial markets
Ann-Christina Lange, Marc Lenglet, and Robert Seyfert. 2016 · 2016
Cited alongside, same era.
A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis
Md. Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017 · 2017
Cited alongside, same era.
Nextgen AML: distributed deep learning based language technologies to augment anti money laundering investigation
Jingguang Han, Utsab Barman, Jer Hayes, Jinhua Du, Edward Burgin, and Dadong Wan. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 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.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir R. Radev. 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.

Then

BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Are you for real? detecting identity fraud via dialogue interactions
Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, and Zhifei Li. 2019 · 2019
Later among the works it cites.
Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth D. Forbus, and Jianfeng Gao. 2020a · 2020
Later among the works it cites.
Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020b · 2020
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. 2020c · 2020
Later among the works it cites.
Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension
Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, and Quoc V. Le. 2020d · 2020
Later among the works it cites.
Point to the expression: Solving algebraic word problems using the expression-pointer transformer model
Bugeun Kim, Kyung Seo Ki, Donggeon Lee, and Gahgene Gweon. 2020 · 2020
Later among the works it cites.
Finbert: A pre-trained financial language representation model for financial text mining
Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. 2020 · 2020
Later among the works it cites.
Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context
Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. 2021 · 2021
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…