Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Table 2: Comparison of F1 scores (%) achieved by different algorithms.
['Dataset Max sequence length', 'CoQA 192', 'CoQA 256', 'CoQA 384', 'CoQA 512', 'QuAC 192', 'QuAC 256', 'QuAC 384', 'QuAC 512']
Results. The performance of the BERT-Large model drops drastically as the maximum sequence length decreases. We see a drop of 8.6% in F1 score on the CoQA dataset and a drop of 27.0% on the QuAC dataset when the maximum input length decreases from 512 to 192. Ablation Analysis. We further study the effect of recurrence alone without RL chunking here. However, we do not observe any improvement when the maximum sequence length is set to 384 or 512.