In natural language processing tasks performance of the models is often
measured with some non-differentiable metric, such as BLEU score. To use
efficient gradient-based methods for optimization, it is a common workaround to
optimize some surrogate loss function. This approach is effective if
optimization of such loss also results in improving target metric. The
corresponding problem is referred to as loss-evaluation mismatch. In the
present work we propose a method for calculation of differentiable lower bound
of expected BLEU score that does not involve computationally expensive sampling
procedure such as the one required when using REINFORCE rule from reinforcement
learning (RL) framework.

本文提出一种计算期望 BLEU 分数的可微下限的方法，避免了使用强化学习（RL）框架的 REINFORCE 规则所需的计算昂贵的采样过程，并解决了语言处理任务中非可微度量指标和最优化所需目标的不匹配问题。

期望 BLEU 得分的可微下限

Differentiable lower bound for expected BLEU score

In this paper, we study a classification problem in which sample labels are
randomly corrupted. In this scenario, there is an unobservable sample with
noise-free labels. However, before being observed, the true labels are
independently flipped with a probability $\rho\in[0,0.5)$, and the random label
noise can be class-conditional. Here, we address two fundamental problems
raised by this scenario. The first is how to best use the abundant surrogate
loss functions designed for the traditional classification problem when there
is label noise. We prove that any surrogate loss function can be used for
classification with noisy labels by using importance reweighting, with
consistency assurance that the label noise does not ultimately hinder the
search for the optimal classifier of the noise-free sample. The other is the
open problem of how to obtain the noise rate $\rho$. We show that the rate is
upper bounded by the conditional probability $P(y|x)$ of the noisy sample.
Consequently, the rate can be estimated, because the upper bound can be easily
reached in classification problems. Experimental results on synthetic and real
datasets confirm the efficiency of our methods.

本文研究了一个分类问题，其中样本标签被随机损坏。我们解决了如何在有标签噪声的情况下最好地利用传统分类问题的丰富代理损失函数，通过重要性重新加权来使用任何代理损失函数进行带有噪声标签的分类，以及如何获得噪声率的问题。