3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)

Reported on 5 benchmarks across 1 task · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing5 results

Fake News DetectiononFNC-1
Per-class Accuracy (Agree)· 2017-07-11
44.04
best: 88.47 (ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022))
SOTA
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task arXiv:1707.03264
Fake News DetectiononFNC-1
Per-class Accuracy (Disagree)· 2017-07-11
6.6
best: 96 (ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022))
SOTA
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task arXiv:1707.03264
Fake News DetectiononFNC-1
Per-class Accuracy (Discuss)· 2017-07-11
81.38
best: 87.7 (ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022))
SOTA
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task arXiv:1707.03264
Fake News DetectiononFNC-1
Per-class Accuracy (Unrelated)· 2017-07-11
97.9
best: 99.36 (Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021))
SOTA
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task arXiv:1707.03264
Fake News DetectiononFNC-1
Weighted Accuracy· 2017-07-11
81.72
best: 90.73 (Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021))
SOTA
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task arXiv:1707.03264