This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about the system operations as possible. Detecting anomalous heterogeneous graphs from a large set of system behaviour graphs is crucial for many real-world applications like online web/mobile service and cloud access control. To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs. HRGCN trains a hierarchical relation-augmented Heterogeneous Graph Neural Network (HetGNN), which learns better graph representations by modelling the interactions among all the system entities and considering both source-to-destination entity (node) types and their relation (edge) types. Extensive evaluation on two real-world application datasets shows that HRGCN outperforms state-of-the-art competing anomaly detection approaches. We further present a real-world industrial case study to justify the effectiveness of HRGCN in detecting anomalous (e.g., congested) network devices in a mobile communication service. HRGCN is available at https://github.com/jiaxililearn/HRGCN.

Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.However, collecting a large amount of turnby-turn annotated dialogue data is costly and inefficient.In this paper, we propose a novel turn-level active learning framework for DST to actively select turns in dialogues to annotate.Given the limited labelling budget, experimental results demonstrate the effectiveness of selective annotation of dialogue turns.Additionally, our approach can effectively achieve comparable DST performance to traditional training approaches with significantly less annotated data, which provides a more efficient way to annotate new dialogue data 1 . Turn User SystemCan you tell me some info on the Avalon hotel?The Avalon is a 4 star moderately priced guesthouse in the north with free internet.Would you like to book there?Yes. Can you book it for 5 people on Saturday?We need rooms for 4 nights. Dialogue StateYour taxi has been booked to take you from Avalon to Frankie and Bennys at 17:45.Your taxi will be a black Tesla and the contact number is 07715682347.That sounds great.Thank you very much.…..

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.Maintaining their up-to-date status is a pressing concern in the current era.This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch.We categorize research works systemically and provide in-depth comparisons and discussion.We also discuss existing challenges and highlight future directions to facilitate research in this field 1 .

Zihan Zhang, Meng Fang, Ling Chen, Mohammad Reza Namazi Rad. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.