HackerPapers

arXiv:1907.07347cs.CL

Fake News Detection as Natural Language Inference

Kai-Chou Yang, Timothy Niven, Hung-Yu Kao

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge. We treat the task as natural language inference (NLI). We individually train a number of the strongest NLI models as well as BERT. We ensemble these results and retrain with noisy labels in two stages. We analyze transitivity relations in the train and test sets and determine a set of test cases that can be reliably classified on this basis. The remainder of test cases are classified by our ensemble. Our entry achieves test set accuracy of 88.063% for 3rd place in the competition.

arXiv comments
Journal reference
DOI
Subjects
cs.CLComputation and Language

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Jul 2019 to Jul 2019. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.