Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models

• URL: http://arxiv.org/abs/2101.06829v2
• Date: Fri, 19 Feb 2021 18:36:31 GMT
• Title: Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
• Authors: Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl
• Abstract summary: We explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders for natural language understanding tasks. Experiments show that EBM training can help the model reach a better calibration that is competitive to strong baselines.
• Score: 61.768082640087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. Our experiments show that EBM training can help the model reach a better calibration that is competitive to strong baselines, with little or no loss in accuracy. We discuss three variants of energy functions (namely scalar, hidden, and sharp-hidden) that can be defined on top of a text encoder, and compare them in experiments. Due to the discreteness of text data, we adopt noise contrastive estimation (NCE) to train the energy-based model. To make NCE training more effective, we train an auto-regressive noise model with the masked language model (MLM) objective.

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