论文标题
分析影响力功能的使用，以便在神经机转换中特定的数据过滤
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation
论文作者
论文摘要
客户反馈可能是改善商用机器翻译系统的重要信号。解决特定翻译错误的一种解决方案是删除相关的错误训练实例，然后重新训练机器翻译系统，我们称之为特定于实例的数据过滤。影响功能（if）已被证明可以有效地找到用于分类任务的相关培训示例，例如图像分类，有毒语音检测和索取任务。给定探测实例，如果通过测量探测实例与梯度空间中的一组训练示例的相似性来找到有影响力的训练示例。在这项工作中，我们检查了影响功能的神经机器翻译（NMT）的使用。我们提出了两种有效的扩展，以表现出最先进的影响功能，并在复制训练示例的子问题上证明，如果可以比手工制作的正则表达式更广泛地应用。
Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. Influence functions (IF) have been shown to be effective in finding such relevant training examples for classification tasks such as image classification, toxic speech detection and entailment task. Given a probing instance, IF find influential training examples by measuring the similarity of the probing instance with a set of training examples in gradient space. In this work, we examine the use of influence functions for Neural Machine Translation (NMT). We propose two effective extensions to a state of the art influence function and demonstrate on the sub-problem of copied training examples that IF can be applied more generally than handcrafted regular expressions.
