Fact Verification
Jan 1, 2017
In this project, we aim to improve the automatic detection of false information. We rely on trustworthy sources and evaluate content claims them.
Publications
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction
Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase …
Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini, Alex Fabrikant, Donald Metzler, Tal Schuster
May 2023
Annual Meeting of the Association for Computational Linguistics (ACL)
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition
The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually …
Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Dan Roth, Tal Schuster
May 2023
Findings of ACL
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Natural Language Inference (NLI) has been extensively studied by the NLP community as a framework for estimating the semantic relation …
Tal Schuster, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Donald Metzler
June 2022
Findings of EMNLP
Robust and Efficient Deep Learning for Misinformation Prevention
Deep learning models have recently revolutionized the online environment, opening up many exciting opportunities to improve the user …
Tal Schuster
September 2021
Massachusetts Institute of Technology
Consistent Accelerated Inference via Confident Adaptive Transformers
We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now …
Tal Schuster*, Adam Fisch*, Tommi Jaakkola, Regina Barzilay
April 2021
In Empirical Methods in Natural Language Processing (EMNLP)
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as …
Tal Schuster, Adam Fisch, Regina Barzilay
March 2021
In North American Chapter of the Association for Computational Linguistics (NAACL)
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction …
Adam Fisch*, Tal Schuster*, Tommi Jaakkola, Regina Barzilay
January 2021
In International Conference on Learning Representations (ICLR)
Distilling the Evidence to Augment Fact Verification Models
The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the …
Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, Enrico Santus
July 2020
In Workshop on Fact Extraction and VERification (FEVER workshop at ACL)
The Limitations of Stylometry for Detecting Machine-Generated Fake News
Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading …
Tal Schuster, Roei Schuster, Darsh J Shah, Regina Barzilay
February 2020
In Computational Linguistics (CL)
Automatic Fact-guided Sentence Modification
Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may …
Darsh J Shah*, Tal Schuster*, Regina Barzilay
January 2020
In AAAI Conference on Artificial Intelligence (AAAI)
Towards Debiasing Fact Verification Models
Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this …
Tal Schuster*, Darsh J Shah*, Yun Jie Serene Yeo, Daniel Filizzola, Enrico Santus, Regina Barzilay
August 2019
In Empirical Methods in Natural Language Processing (EMNLP)