It refers to the critique (Ranzato et al., 2016) that MLE-trained models tend to have suboptimal performance as they are trained to maximize a convenient objective (i.e., maximum likelihood of word-level correct next step prediction) rather than a desirable sequence-level objective that correlates better with the common discrete evaluation metrics such as ROUGE (Lin and Och, 2004) for summarization, BLEU (Papineni et al., 2002) for translation, and word error rate for speech recognition, not loglikelihood.
training models that directly optimize for such discrete metrics as objective is hard due to non-differentiable nature of the corresponding loss functions (Rosti et al., 2011).