Effective root cause analysis (RCA) is vital for swiftly restoring services,
minimizing losses, and ensuring the smooth operation and management of complex
systems. Previous data-driven RCA methods, particularly those employing causal
discovery techniques, have primarily focused on constructing dependency or
causal graphs for backtracking the root causes. However, these methods often
fall short as they rely solely on data from a single modality, thereby
resulting in suboptimal solutions. In this work, we propose Mulan, a unified
multi-modal causal structure learning method for root cause localization. We
leverage a log-tailored language model to facilitate log representation
learning, converting log sequences into time-series data. To explore intricate
relationships across different modalities, we propose a contrastive
learning-based approach to extract modality-invariant and modality-specific
representations within a shared latent space. Additionally, we introduce a
novel key performance indicator-aware attention mechanism for assessing
modality reliability and co-learning a final causal graph. Finally, we employ
random walk with restart to simulate system fault propagation and identify
potential root causes. Extensive experiments on three real-world datasets
validate the effectiveness of our proposed framework.

本研究提出了一种统一的多模态因果结构学习方法 Mulna，通过一个定制的语言模型来学习日志表示，将日志序列转化为时间序列数据，并采用基于对比学习的方法提取多模态中不变和特定于模态的表示。此外，引入了一种考虑关键性能指标的注意机制来评估模态可靠性，并共同学习最终的因果图。最后，通过随机游走重启来模拟系统故障传播并识别潜在的根本原因。对三个真实世界数据集的广泛实验证实了我们提出的框架的有效性。

多模态因果结构学习与根本原因分析

Multi-modal Causal Structure Learning and Root Cause Analysis

Audio-visual speech recognition (AVSR) has gained remarkable success for
ameliorating the noise-robustness of speech recognition. Mainstream methods
focus on fusing audio and visual inputs to obtain modality-invariant
representations. However, such representations are prone to over-reliance on
audio modality as it is much easier to recognize than video modality in clean
conditions. As a result, the AVSR model underestimates the importance of visual
stream in face of noise corruption. To this end, we leverage visual
modality-specific representations to provide stable complementary information
for the AVSR task. Specifically, we propose a reinforcement learning (RL) based
framework called MSRL, where the agent dynamically harmonizes
modality-invariant and modality-specific representations in the auto-regressive
decoding process. We customize a reward function directly related to
task-specific metrics (i.e., word error rate), which encourages the MSRL to
effectively explore the optimal integration strategy. Experimental results on
the LRS3 dataset show that the proposed method achieves state-of-the-art in
both clean and various noisy conditions. Furthermore, we demonstrate the better
generality of MSRL system than other baselines when test set contains unseen
noises.

我们提出了一个基于强化学习的框架（MSRL），动态地协调模态不变和模态特异性的表示，从而稳定地提供互补信息，用于音视频语音识别任务，实验结果表明，此方法在 LRS3 数据集中取得了最新的成果。