Wei Fang


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Neural Multi-Task Learning for Stance Prediction
Wei Fang | Moin Nadeem | Mitra Mohtarami | James Glass
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.

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FAKTA: An Automatic End-to-End Fact Checking System
Moin Nadeem | Wei Fang | Brian Xu | Mitra Mohtarami | James Glass
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

We present FAKTA which is a unified framework that integrates various components of a fact-checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions.


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Entity Disambiguation by Knowledge and Text Jointly Embedding
Wei Fang | Jianwen Zhang | Dilin Wang | Zheng Chen | Ming Li
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning