大規模要約資源としてのNew York Times Annotated Corpus

Generalized zero-shot learning aims to recognize images from seen and unseen domains. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem. In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately. Specifically, we explore cross-attentive second-order visual statistics to compact the semantic-free representation, and design an adaptive margin Softmax to maximize inter-class divergences. Thus, the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images, i.e., domain detection, based on the predicted class entropy. For unseen images, we automatically search an optimal semantic-visual alignment architecture, rather than manual designs, to predict unseen classes. With accurate domain detection, the biased recognition problem towards the seen domain is significantly reduced. Experiments on five benchmarks for classification and segmentation show that DVBE outperforms existing methods by averaged 5.7% improvement.

/pdf/domain-aware-visual-bias-eliminating-for-generalized-zero-5bq9fpeu7g.pdf

Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning

Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem. In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately. Specifically, we explore cross-attentive second-order visual statistics to compact the semantic-free representation, and design an adaptive margin Softmax to maximize inter-class divergences. Thus, the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images, i.e., domain detection, based on the predicted class entropy. For unseen images, we automatically search an optimal semantic-visual alignment architecture, rather than manual designs, to predict unseen classes. With accurate domain detection, the biased recognition problem towards the seen domain is significantly reduced. Experiments on five benchmarks for classification and segmentation show that DVBE outperforms existing methods by averaged 5.7% improvement.

Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning

ive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance. Compared with Transformers, topic models are better at learning explicit document semantics, and hence could be integrated into Transformers to further boost their performance. To this end, we rearrange and explore the semantics learned by a topic model, and then propose a topic assistant (TA) including three modules. TA is compatible with various Transformer-based models and user-friendly since i) TA is a plug-and-play model that does not break any structure of the original Transformer network, making users easily fine-tune Transformer+TA based on a well pre-trained model; ii) TA only introduces a small number of extra parameters. Experimental results on three datasets demonstrate that TA is able to improve the performance of several Transformer-based models.

Friendly Topic Assistant for Transformer Based Abstractive Summarization

How reliably an automatic summarization evaluation metric replicates human judgments of summary quality is quantified by system-level correlations. We identify two ways in which the definition of the system-level correlation is inconsistent with how metrics are used to evaluate systems in practice and propose changes to rectify this disconnect. First, we calculate the system score for an automatic metric using the full test set instead of the subset of summaries judged by humans, which is currently standard practice. We demonstrate how this small change leads to more precise estimates of system-level correlations. Second, we propose to calculate correlations only on pairs of systems that are separated by small differences in automatic scores which are commonly observed in practice. This allows us to demonstrate that our best estimate of the correlation of ROUGE to human judgments is near 0 in realistic scenarios. The results from the analyses point to the need to collect more high-quality human judgments and to improve automatic metrics when differences in system scores are small.

Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics

Constructing a graph with graph convolutional network (GCN) to explore the relational structure of the data has attracted lots of interests in various tasks. However, for document classification, existing graph based methods often focus on the straightforward word-word and word-document relations, ignoring the hierarchical semantics. Besides, the graph construction is often independent from the task-specific GCN learning. To address these constrains, we integrate a probabilistic deep topic model into graph construction, and propose a novel trainable hierarchical topic graph (HTG), including word-level, hierarchical topic-level and document-level nodes, exhibiting semantic variation from finegrained to coarse. Regarding the document classification as a document-node label generation task, HTG can be dynamically evolved with GCN by performing variational inference, which leads to an end-to-end document classification method, named dynamic HTG (DHTG). Besides achieving state-of-the-art classification results, our model learns an interpretable document graph with meaningful node embeddings and semantic edges.

/pdf/learning-dynamic-hierarchical-topic-graph-with-graph-4pakctak84.pdf

Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification.

Anomaly detection within multivariate time series (MTS) is an essential task in both data mining and service quality management. Many recent works on anomaly detection focus on de-signing unsupervised probabilistic models to extract robust normal patterns of MTS. In this paper, we model channel dependency and stochasticity within MTS by developing an embedding-guided probabilistic generative network. We combine it with adaptive V ariational G raph C onvolutional R ecurrent N etwork (VGCRN) to model both spatial and temporal fine-grained correlations in MTS. To explore hierarchical latent representations, we further extend VGCRN into a deep variational network, which captures multilevel information at different layers and is robust to noisy time series. Moreover, we develop an upward-downward variational inference scheme that considers both forecasting-based and reconstruction-based losses, achieving an accurate posterior ap-proximation of latent variables with better MTS representations. The experiments verify the superiority of the proposed method over the current state of the art.

Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embeddings to represent words and topics. With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics. Furthermore, due to the superiority of hyperbolic geometry in representing hierarchical data, tree-structure knowledge can also be naturally injected to guide the learning of a topic hierarchy. Therefore, we further develop a regularization term based on the idea of contrastive learning to inject prior structural knowledge efficiently. Experiments on both topic taxonomy discovery and document representation demonstrate that the proposed framework achieves improved performance against existing embedded topic models.

HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding

Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers. To relax this assumption, we propose sawtooth factorial topic embedding guided GBN, a deep generative model of documents that captures the dependencies and semantic similarities between the topics in the embedding space. Specifically, both the words and topics are represented as embedding vectors of the same dimension. The topic matrix at a layer is factorized into the product of a factor loading matrix and a topic embedding matrix, the transpose of which is set as the factor loading matrix of the layer above. Repeating this particular type of factorization, which shares components between adjacent layers, leads to a structure referred to as sawtooth factorization. An auto-encoding variational inference network is constructed to optimize the model parameter via stochastic gradient descent. Experiments on big corpora show that our models outperform other neural topic models on extracting deeper interpretable topics and deriving better document representations.

/pdf/sawtooth-factorial-topic-embeddings-guided-gamma-belief-5bvjxuo52e.pdf

Zhibin Duan

Papers

Friendly Topic Assistant for Transformer Based Abstractive Summarization

Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification.

Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network