We present a conditional text generation framework that posits sentential
expressions of possible causes and effects. This framework depends on two novel
resources we develop in the course of this work: a very large-scale collection
of English sentences expressing causal patterns CausalBank; and a refinement
over previous work on constructing large lexical causal knowledge graphs Cause
Effect Graph. Further, we extend prior work in lexically-constrained decoding
to support disjunctive positive constraints. Human assessment confirms that our
approach gives high-quality and diverse outputs. Finally, we use CausalBank to
perform continued training of an encoder supporting a recent state-of-the-art
model for causal reasoning, leading to a 3-point improvement on the COPA
challenge set, with no change in model architecture.

本研究提出了一种基于条件的文本生成框架，依赖于 CausalBank 和 Cause Effect Graph 两个新资源开发，并使用词汇约束解码来支持析取正约束，利用 CausalBank 对最近一种最先进的因果推理模型的编码器进行持续训练，在不改变模型架构的情况下在 COPA 挑战集上取得了 3 分的提高。

指导式因果关系生成

Guided Generation of Cause and Effect

Current models for event causality identification (ECI) mainly adopt a
supervised framework, which heavily rely on labeled data for training.
Unfortunately, the scale of current annotated datasets is relatively limited,
which cannot provide sufficient support for models to capture useful indicators
from causal statements, especially for handing those new, unseen cases. To
alleviate this problem, we propose a novel approach, shortly named CauSeRL,
which leverages external causal statements for event causality identification.
First of all, we design a self-supervised framework to learn context-specific
causal patterns from external causal statements. Then, we adopt a contrastive
transfer strategy to incorporate the learned context-specific causal patterns
into the target ECI model. Experimental results show that our method
significantly outperforms previous methods on EventStoryLine and
Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

本文提出了 CauSeRL 方法，基于自我监督和对比转移策略，通过利用外部因果语句来识别事件因果关系，实现从环境上学习因果模式并提高模型的性能。实验结果表明，该方法在 EventStoryLine 和 Causal-TimeBank 上比先前研究的方法分别提高 2.0 和 3.4 个 F1 值点。

通过对外部因果语句进行自监督表示学习来改进事件因果关系识别

Improving Event Causality Identification via Self-Supervised  Representation Learning on External Causal Statement

The recent increase in the scale and complexity of software systems has
introduced new challenges to the time series monitoring and anomaly detection
process. A major drawback of existing anomaly detection methods is that they
lack contextual information to help stakeholders identify the cause of
anomalies. This problem, known as root cause detection, is particularly
challenging to undertake in today's complex distributed software systems since
the metrics under consideration generally have multiple internal and external
dependencies. Significant manual analysis and strong domain expertise is
required to isolate the correct cause of the problem. In this paper, we propose
a method that isolates the root cause of an anomaly by analyzing the patterns
in time series fluctuations. Our method considers the time series as
observations from an underlying process passing through a sequence of
discretized hidden states. The idea is to track the propagation of the effect
when a given problem causes unaligned but homogeneous shifts of the underlying
states. We evaluate our approach by finding the root cause of anomalies in
Zillows clickstream data by identifying causal patterns among a set of observed
fluctuations.

提出了一种基于时间序列波动模式分析的方法，以定位分布式软件系统中异常的根本原因，并在 Zillows 的点击流数据上进行了验证。