Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network compatible with different anomaly detection methods. To address the non-prominence of anomalies, we design main element binarization (MEBin) to obtain anomaly-centered images, ensuring anomalies are learned while avoiding the impact of incorrect detections. Next, to learn anomalies with weak semantics, we design mask-guided representation learning, which focuses on isolated anomalies guided by masks and reduces confusion from erroneous inputs through corrected pseudo labels. Finally, to enable flexible classification at both region and image levels, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% F1gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, and 12.8% F1gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. Code is available at https://github.com/HUST-SLOW/AnomalyNCD.

This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial product images, the normal image patches could find a relatively large number of similar patches in other unlabeled images, while the abnormal ones only have a few similar patches. We leverage such a discriminative characteristic to design a novel zero-shot AC/AS method by Mutual Scoring (MuSc) of the unlabeled images, which does not need any training or prompts. Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees (LNAMD) to obtain the patch features that are capable of representing anomalies in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to leverage the unlabeled test images to assign the anomaly score to each other. Furthermore, we present an optimization approach named Re-scoring with Constrained Image-level Neighborhood (RsCIN) for image-level anomaly classification to suppress the false positives caused by noises in normal images. The superior performance on the challenging MVTec AD and VisA datasets demonstrates the effectiveness of our approach. Compared with the state-of-the-art zero-shot approaches, MuSc achieves a $\textbf{21.1}$% PRO absolute gain (from 72.7\% to 93.8\%) on MVTec AD, a $\textbf{19.4}$% pixel-AP gain and a $\textbf{14.7}$% pixel-AUROC gain on VisA. In addition, our zero-shot approach outperforms most of the few-shot approaches and is comparable to some one-class methods. Code is available at https://github.com/xrli-U/MuSc.

Currently, human-bot symbiosis dialog systems, e.g. pre- and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall efficiency, reduce human resource cost and increase user experience. To satisfy this requirement, existing methods are mostly heuristic and cannot obtain high-quality performance. In this paper, we investigate the important problem by thoroughly mining both the data-to-task and task-to-task knowledge among various kinds of dialog data. To achieve the above target, we propose a comprehensive and general solution with multi-task learning framework, specifically including a novel dialog encoder and two tailored gated mechanism modules. The proposed Gated Mechanism enhanced Multi-task Model (G3M) can play the role of hierarchical information filtering and is non-invasive to the existing dialog systems. Experiments on two datasets collected from the real world demonstrate our method’s effectiveness and the results achieve the state-of-the-art performance by relatively increasing 8.7%/11.8% on RMSE metric and 2.2%/4.4% on F1 metric.

MOTIVATION: The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-consuming, especially for the exact determination of micro-pKa information at the atomic level. Hence, a fast and accurate prediction of pKa values of chemical compounds is of broad interest. RESULTS: Here, we compiled a large-scale pKa dataset containing 16 595 compounds with 17 489 pKa values. Based on this dataset, a novel pKa prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro-pKa values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pKa was also able to automatically deconvolute the predicted macro-pKa into discrete micro-pKa values. AVAILABILITY AND IMPLEMENTATION: The Graph-pKa model is now freely accessible via a web-based interface (https://pka.simm.ac.cn/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dialog Router is a general paradigm for human-bot symbiosis dialog systems to provide friendly customer care service. It is equipped with a multi-task learning model to automatically capture the underlying correlation between multiple related tasks, i.e. dialog classification and regression, and greatly reduce human labor work for system customization, which improves the accuracy of dialog transition. In addition, for learning the multi-task model, the training data and labels are easy to collect from human-to-human historical dialog logs, and the Dialog Router can be easily integrated into the majority of existing dialog systems by calling general APIs. We conduct experiments on real-world datasets for dialog classification and regression. The results show that our model achieves improvements on both tasks, which benefits the dialog transition application. The demo illustrates our method’s effectiveness in a real customer care service.

Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. In this way, we explicitly capture the character information and the relations between plots and characters to improve explainability and consistency. Experimental results on open dataset indicate that our model yields meaningful improvements over several strong baselines on both human and automatic evaluations.

Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.However, existing methods either take too many manual efforts (e.g.reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g.sequence-to-sequence methods).In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient.Our method is data-driven and does not require too much manual work for intervention during system design.We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.

Balanced clustering aims at partitioning a dataset with roughly even cluster sizes while exploiting the intrinsic structure of the data. Despite attracting increased attention recently in both the academia and the industry, most existing balanced clustering algorithms still have high run time complexities that prevent them from being applied to large datasets. To cope with this challenge, we propose a Fast Clustering with Flexible balance Constraints FCFC, a simple, fast and effective clustering algorithm that can deal with flexible balance constraints. In essence, FCFC employs K-means as the core clustering algorithm and the cluster size variances as the penalty for imbalance. The objective function consists of the combined classical K-means clustering cost as well as the imbalance penalty. By exploiting a new insight of the second term, FCFC is able to employ an efficient K-means-like optimization procedure that can scale to big datasets. Furthermore, we also extend our model for multiple balance constraints with theoretical supports. Extensive experimental results show that our method exceeds several state-of-the-art methods by large margins in terms of efficiency and clustering quality. Finally, a real-world application for Bing search is provided, where data are organized in multiple machines with data size and query frequency balancing objectives. In the simulated scenario, our solution achieves the same fidelity score while reduces cost by 75% compared to the baseline method.