Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
Table 3: Effect of claim-article pair sequence length of FNC-I test set on classification accuracy of RoBERTa model, with a maximum sequence length of 512.
['Number of Tokens in Example', 'Acc', 'Number of Examples']
The model has a maximum sequence length of 512 tokens, so any examples longer than this are trimmed. We find that the model performs best for examples that utilize the full capacity of the input sequence (385 to 512 tokens). Very short sequences (<129 tokens) provide the least amount of information to the model, and the model performs poorly. Long sequences (>512 tokens) have some of their context removed from their input, and these examples also perform relatively poor.