QUANTIFYING EXPOSURE BIAS FOR OPEN-ENDED LANGUAGE GENERATION Anonymous authors Paper under double-blind review

Abstract

The exposure bias problem refers to the incrementally distorted generation induced by the training-generation discrepancy, in teacher-forcing training for autoregressive neural network language models (LM). It has been regarded as a central problem for LMs trained for open-ended language generation. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we propose novel metrics to quantify the impact of exposure bias in the generation of MLE-trained LMs. Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed. We conduct both automatic and human evaluation in our experiments, and our observations are two-fold: (1) We confirm that the prefix discrepancy indeed induces some level of performance loss. (2) However, the induced distortion seems to be limited, and is not incremental during the generation, which contradicts the claim of exposure bias.

1. INTRODUCTION

Language model (LM) is a central module for natural language generation (NLG) tasks (Young et al., 2017 ) such as open-ended language generation (Radford et al., 2018; Nadeem et al., 2020) , machine translation (Wu et al., 2017) , dialogue response generation (Li et al., 2017) , image captioning (Lin et al., 2014) , etc. For decades, maximum likelihood estimation (MLE) has been the most widely used objective for LM training. However, there is a popular belief in the natural language processing (NLP) community that standard MLE training suffers from the exposure bias problem which leads to an incremental performance degradation during test-time generation. The claim of the exposure bias problem (Bengio et al., 2015; Ranzato et al., 2016) is originated from the following discrepancy between MLE training and test-time generation for auto-regressive language models: During training, the model is trained to predict the next word conditioned on prefix (or history) words sampled from the ground-truth data distribution; While during generation, the model generates words conditioned on prefix sequences generated by the model itself. Hence, due to the exposure to real data during training, the language model could potentially be biased to only perform well with data prefixes. Therefore, it is claimed (and widely believed among researchers) that during generation the errors should accumulate along the generated sequence, and the distribution generated by the model would be incrementally distorted. The forced exposure to ground-truth data during training is also referred to as teacher forcing. In order to avoid teacher forcing, many training algorithms (Bengio et al., 2015; Lamb et al., 2016; Ranzato et al., 2016; Yu et al., 2016; Zhu et al., 2018; Lu et al., 2018; Lin et al., 2017; Guo et al., 2017; Rajeswar et al., 2017; Wiseman & Rush, 2016; Nie et al., 2019; Shi et al., 2018; de Masson d'Autume et al., 2019; Rennie et al., 2016) have been proposed as alternatives to MLE training for open-ended language generation. Most of these works utilize techniques from generative adversarial networks (GANs) (Goodfellow et al., 2014) or reinforcement learning (RL) (Sutton & Barto, 1998) . In this paper, we refer to these algorithms as non-MLE methods. With the huge research efforts devoted to alleviate exposure bias, interestingly, the existence or significance of exposure bias is much less studied. On the other hand, despite the criticism, MLE

