text:   Understanding Models",6,"['New paper on,\n- Analyzing calibration in NLU models\n- How to improve it using noise contrastive estimation training \nAccepted to EACL 2021 \n(<LINK>)\n@SFResearch [1/6] <LINK>', 'We explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. [2/6] https://t.co/a4vzRQb3K0', 'In most tasks, all three EBM variants get substantial improvement in ECE with little or no loss in accuracy comparing to the (strong) baseline methods. [3/6] https://t.co/1nIvYVAYWZ', 'We plot how test-set ECE changes during training. It is shown as the training reaches the high-accuracy area, the calibration for baseline model becomes worse, while EBM training is able to reach a better trade-off between accuracy and calibration. [4/6] https://t.co/ZLtuWiikYq', 'How does the model get better calibration? It is shown that models trained with the hidden and sharp-hidden variants tend to assign more conservative predictions (reflected by higher entropy) for higher energy (less likely) samples. [5/6] https://t.co/VcWZFCwual', 'We suspect this is due to the strong coupling between the energy function and the classification logits. We provide concrete examples here. \nHowever, we need to mention that we do not observe this interesting trend (Figure 4) in all datasets (e.g., QNLI) [6/6] https://t.co/7MTVdpldWR']",21,01,1289