Towards Debiasing Fact Verification Models

Identifying and correcting label bias in machine learning

Original

Heinrich Jiang and Ofir Nachum. 2019 · 1901

Earlier work this paper cites.

A coefficient of agreement for nominal scales

Jacob Cohen. 1960 · 1960

Earlier work this paper cites.

The statistics of word cooccurrences: word pairs and collocations

Stefan Evert. 2005 · 2005

Earlier work this paper cites.

Glove: Global vectors for word representation

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014

Earlier work this paper cites.

A large annotated corpus for learning natural language inference

Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015

Earlier work this paper cites.

The goldilocks principle: Reading children’s books with explicit memory representations

Original

Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016

Earlier work this paper cites.

Who did what: A large-scale person-centered cloze dataset

Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David A. McAllester. 2016 · 2016

Earlier work this paper cites.

Towards ai-complete question answering: A set of prerequisite toy tasks

Original

Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov. 2016 · 2016

Earlier work this paper cites.

Enhanced lstm for natural language inference

Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017

Cited alongside, same era.

Allennlp: A deep semantic natural language processing platform

Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017

Cited alongside, same era.

Where is your evidence: Improving fact-checking by justification modeling

Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018

Cited alongside, same era.

Annotation artifacts in natural language inference data

Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018

Cited alongside, same era.

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.

Deep contextualized word representations

Fever: a large-scale dataset for fact extraction and verification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018

Later among the works it cites.

BERT: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

Closest in time.

Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference

Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019

Closest in time.

Combining fact extraction and verification with neural semantic matching networks

Original

Yixin Nie, Haonan Chen, and Mohit Bansal. 2019 · 2019

Closest in time.

Probing neural network comprehension of natural language arguments

Timothy Niven and Hung-Yu Kao. 2019 · 2019

Closest in time.