We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference. We show that models can capture human judgement distribution by applying additional distribution estimation methods, namely, Monte Carlo (MC) Dropout, Deep Ensemble, Re-Calibration, and Distribution Distillation. All four of these methods substantially outperform the softmax baseline. We show that MC Dropout is able to achieve decent performance without any distribution annotations while Re-Calibration can further give substantial improvements when extra distribution annotations are provided, suggesting the value of multiple annotations for the example in modeling the distribution of human judgements. Moreover, MC Dropout and Re-Calibration can achieve decent transfer performance on out-of-domain data. Despite these improvements, the best results are still far below estimated human upper-bound, indicating that the task of predicting the distribution of human judgements is still an open, challenging problem with large room for future improvements. We showcase the common errors for MC Dropout and Re-Calibration. Finally, we give guidelines on the usage of these methods with different levels of data availability and encourage future work on modeling the human opinion distribution for language reasoning.

In the last article (click here), we briefly talked about the basics of ANN technique. But before using the technique, an analyst must know, how does the technique really work? Even though the detailed derivation may not be required, one should know the framework of the algorithm. This article will provide you a basic understanding of Artificial Neural Network (ANN) framework. We won't go into actual derivation, but the information provided in this article will be sufficient for you to appreciate and implement the algorithm.