Before revision:  We show that this approach outperforms other methods with a similar number of trainable parameters on SST-2 and MNLI datasets.
After revision:  Using up to 25K trainable parameters per task, this approach outperforms other methods with a similar number of trainable parameters on SST-2 and MNLI datasets.