Bryan R. Routledge

Also published as: Bryan Routledge


2021

pdf bib
FinQA: A Dataset of Numerical Reasoning over Financial Data
Zhiyu Chen | Wenhu Chen | Charese Smiley | Sameena Shah | Iana Borova | Dylan Langdon | Reema Moussa | Matt Beane | Ting-Hao Huang | Bryan Routledge | William Yang Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset – the first of its kind – should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available at https://github.com/czyssrs/FinQA.

2016

pdf bib
Friends with Motives: Using Text to Infer Influence on SCOTUS
Yanchuan Sim | Bryan Routledge | Noah A. Smith
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Character Sequence Models for Colorful Words
Kazuya Kawakami | Chris Dyer | Bryan Routledge | Noah A. Smith
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

pdf bib
A Utility Model of Authors in the Scientific Community
Yanchuan Sim | Bryan Routledge | Noah A. Smith
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

pdf bib
Dynamic Language Models for Streaming Text
Dani Yogatama | Chong Wang | Bryan R. Routledge | Noah A. Smith | Eric P. Xing
Transactions of the Association for Computational Linguistics, Volume 2

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise difficult to capture using text data by itself. We learn our model in an efficient online fashion that is scalable for large, streaming data. With five streaming datasets from two different genres—economics news articles and social media—we evaluate our model on the task of sequential language modeling. Our model consistently outperforms competing models.

2012

pdf bib
Word Salad: Relating Food Prices and Descriptions
Victor Chahuneau | Kevin Gimpel | Bryan R. Routledge | Lily Scherlis | Noah A. Smith
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

pdf bib
Predicting a Scientific Community’s Response to an Article
Dani Yogatama | Michael Heilman | Brendan O’Connor | Chris Dyer | Bryan R. Routledge | Noah A. Smith
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Predicting Risk from Financial Reports with Regression
Shimon Kogan | Dimitry Levin | Bryan R. Routledge | Jacob S. Sagi | Noah A. Smith
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics