Shortage of available training data is holding back progress in the area of
automated error detection. This paper investigates two alternative methods for
artificially generating writing errors, in order to create additional
resources. We propose treating error generation as a machine translation task,
where grammatically correct text is translated to contain errors. In addition,
we explore a system for extracting textual patterns from an annotated corpus,
which can then be used to insert errors into grammatically correct sentences.
Our experiments show that the inclusion of artificially generated errors
significantly improves error detection accuracy on both FCE and CoNLL 2014
datasets.

本研究探讨了两种替代方法：机器翻译和文本模式提取以解决短缺的数据对自动化错误检测的影响。实验表明：人工生成的错误明显提高了对 FCE 和 CoNLL 2014 数据集的错误检测精度。

利用机器翻译和句法模式进行人造错误生成

Artificial Error Generation with Machine Translation and Syntactic  Patterns

One of the major open challenges in self-driving cars is the ability to
detect cars and pedestrians to safely navigate in the world. Deep
learning-based object detector approaches have enabled great advances in using
camera imagery to detect and classify objects. But for a safety critical
application, such as autonomous driving, the error rates of the current state
of the art are still too high to enable safe operation. Moreover, the
characterization of object detector performance is primarily limited to testing
on prerecorded datasets. Errors that occur on novel data go undetected without
additional human labels. In this letter, we propose an automated method to
identify mistakes made by object detectors without ground truth labels. We show
that inconsistencies in the object detector output between a pair of similar
images can be used as hypotheses for false negatives (e.g., missed detections)
and using a novel set of features for each hypothesis, an off-the-shelf binary
classifier can be used to find valid errors. In particular, we study two
distinct cues - temporal and stereo inconsistencies - using data that are
readily available on most autonomous vehicles. Our method can be used with any
camera-based object detector and we illustrate the technique on several sets of
real world data. We show that a state-of-the-art detector, tracker, and our
classifier trained only on synthetic data can identify valid errors on KITTI
tracking dataset with an average precision of 0.94. We also release a new
tracking dataset with 104 sequences totaling 80,655 labeled pairs of stereo
images along with ground truth disparity from a game engine to facilitate
further research. The dataset and code are available at
this https URL

本文讨论使用深度学习的物体检测方法在自动驾驶汽车中出现的错误，提出了一种自动化方法来识别没有基准标签的物体检测器的错误，重点研究了时间和立体不一致性这两个方面的错检，并展示了数据集和代码供进一步研究。