Unifying Question Answering, Text Classification, and Regression via Span Extraction
Table 2: Performance metrics on the GLUE tasks. We use Matthew’s correlation for CoLA, an average of the Pearson and Spearman correlation for STS, and exact match accuracy for all others. Bold marks the best performance for a task in a section delimited by double horizontal lines. Scores for MNLI are averages of matched and mismatched scores. (→A) indicates that a model was fine-tuned on A as an intermediate task before fine-tuning on a target task (the task header for any particular column). In cases where A and the target task are the same, no additional fine-tuning is done. The phrase on STILTs indicates that test set scores on the target task are the result of testing with the best (→A) according to development scores.
['[EMPTY]', 'SST', 'MRPC', 'QQP', 'MNLI', 'RTE', 'QNLI', 'CoLA', 'STS', 'GLUE']
Wang et al. For almost all datasets, the performance for SpEx-BERT is better than that of BERT, which is perhaps especially surprising for the regression task (STS-B). One can reasonably expect model performance to improve by converting such problems into a span-extraction problem over natural language class descriptions.