Adaptive Interaction Fusion Networks for Fake News Detection
Table 3: Comparison between AIFN and simplified models on RumourEval and PHEME.
['[EMPTY]', 'RumourEval A(%)', 'RumourEval P(%)', 'RumourEval R(%)', 'RumourEval F1(%)', 'PHEME A(%)', 'PHEME P(%)', 'PHEME R(%)', 'PHEME F1(%)']
BERT+BiLSTM applies BERT to BiLSTM model. BERT+BiLSTM+SFSN adds semantic-level fusion self-attention networks (SFSN) to BERT+BiLSTM. BERT+BiLSTM+SFSN+GAIN means BERT+BiLSTM+SFSN incorporating gated adaptive interaction networks (GAIN), i.e., AIFN. Glove+BiLSTM+SFSN+GAIN uses Glove [pennington2014glove] as word embeddings of AIFN. Compared with BERT+BiLSTM+SFSN +GAIN and Glove+BiLSTM+SFSN+GAIN, the former achieves 2.85% and 1.65% boosts in accuracy on RumourEval and PHEME respectively. It proves the effectiveness of BERT including feature ensemble. Effectiveness of SFSN. BERT+BiLSTM+SFSN boosts the performance as compared with BERT+BiLSTM, showing 4.70% and 5.66% improvements in accuracy on the two datasets, which reveals that SFSN enhancing semantic associations and fusion is effective. Effectiveness of GAIN. Compared to BERT+BiLSTM+SFSN, BERT+BiLSTM+SFSN+GAIN notably improves the performance (2.65% and 1.49% boosts) on the two datasets with the help of conflicting semantics obtained by GAIN. It explains the effectiveness of GAIN.