Coreferential Reasoning Learningfor Language Representation
Table 6: Test set performance metrics on GLUE task. The number below each task denotes the number of training examples. Matched/mistached accuracies are reported for MNLI; F1 scores are reported for QQP and MRPC, Spearmanr correlation is reported for STS-B; Accuracy scores are reported for the other tasks.
['Model', 'MNLI-(m/mm)', 'QQP', 'QNLI', 'SST-2', 'CoLA', 'STS-B', 'MRPC', 'RTE']
We notice that CorefBERT achieves comparable results to BERT. Though GLUE does not require much coreference resolution ability due to its attributes, the results prove that our masking strategy and auxiliary training objective would not weaken the performance on natural language understanding tasks.