Event log records all events that occur during the execution of business
processes, so detecting and correcting anomalies in event log can provide
reliable guarantee for subsequent process analysis. The previous works mainly
include next event prediction based methods and autoencoder-based methods.
These methods cannot accurately and efficiently detect anomalies and correct
anomalies at the same time, and they all rely on the set threshold to detect
anomalies. To solve these problems, we propose a business process anomaly
correction method based on Transformer autoencoder. By using self-attention
mechanism and autoencoder structure, it can efficiently process event sequences
of arbitrary length, and can directly output corrected business process
instances, so that it can adapt to various scenarios. At the same time, the
anomaly detection is transformed into a classification problem by means of
selfsupervised learning, so that there is no need to set a specific threshold
in anomaly detection. The experimental results on several real-life event logs
show that the proposed method is superior to the previous methods in terms of
anomaly detection accuracy and anomaly correction results while ensuring high
running efficiency.

基于 Transformer 自编码器的业务过程异常修正方法能够高效处理任意长度的事件序列，并且可以直接输出修正后的业务过程实例，从而适应各种场景，通过自监督学习将异常检测转化为分类问题，无需设置特定阈值，在确保高运行效率的同时，在异常检测准确性和异常修正结果方面优于以往方法。

使用 Transformer 自编码器进行业务流程的异常修正

Anomaly Correction of Business Processes Using Transformer Autoencoder

Cross-language learning allows us to use training data from one language to
build models for a different language. Many approaches to bilingual learning
require that we have word-level alignment of sentences from parallel corpora.
In this work we explore the use of autoencoder-based methods for cross-language
learning of vectorial word representations that are aligned between two
languages, while not relying on word-level alignments. We show that by simply
learning to reconstruct the bag-of-words representations of aligned sentences,
within and between languages, we can in fact learn high-quality representations
and do without word alignments. Since training autoencoders on word
observations presents certain computational issues, we propose and compare
different variations adapted to this setting. We also propose an explicit
correlation maximizing regularizer that leads to significant improvement in the
performance. We empirically investigate the success of our approach on the
problem of cross-language test classification, where a classifier trained on a
given language (e.g., English) must learn to generalize to a different language
(e.g., German). These experiments demonstrate that our approaches are
competitive with the state-of-the-art, achieving up to 10-14 percentage point
improvements over the best reported results on this task.

本文探讨了不依赖词级对齐的跨语言自编码器方法，学习在两种语言之间对齐的词矢量表示，并提出了一种显式的相关性最大化正则化器来提高性能，并在跨语言测试分类问题上进行了实证研究，其结果与现有研究表现相当甚至取得了 10-14 个百分点的提高。