A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Table 2: Confusion matrix of UCLMR’s FNC-1 submission.
['TruePred.', '‘agree’', '‘disagree’', '‘discuss’', '‘unrelated’', 'Overall', '% Accuracy']
We conclude that although our system performs satisfactorily in general, this can mainly be attributed to the close to perfect classification of the instances into ‘related’ and ‘unrelated’ headline/body pairs (accuracy: 96.55%) and the more or less default ‘discuss’ classification of the ‘related’ instances.