Submissions:2019/Cure My FEVER : Building, Breaking and Fixing Models for Fact-Checking

From WikiConference North America
Jump to: navigation, search

This submission has been accepted for WikiConference North America 2019.


Cure My FEVER : Building, Breaking and Fixing Models for Fact-Checking


Reliability of Information
+ Tech & Tools

Type of session:



Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the Fact Extraction and VERification (FEVER) dataset, which involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. We describe results across three phases: the development of a fact-checking system, the creation of adversarial examples, and the improvement of the system in handling adversarial examples.

Academic Peer Review option:


Author name:

Tariq Alhindi

E-mail address:

Wikimedia username:

Affiliated organization(s):

Columbia University

Estimated time:

30 min

Preferred room size:

Special requests:

Have you presented on this topic previously? If yes, where/when?:

If your submission is not accepted, would you be open to presenting your topic in another part of the program? (e.g. lightning talk or unconference session)