To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models. Different from previous methods, word embedding is taken as a probabilistic generative model, and it enables us to impose a prior regularizing word representation learning. The proposed prior not only enhances the representation of embedding vectors but also improves the model's robustness and stability. The structure of the proposed prior is simple and effective, and it can be easily implemented and flexibly plugged in most existing word embedding models. Extensive experiments show the proposed method improves word representation on various tasks.

Lifelong seq2seq language generation models are trained with multiple domains in a lifelong learning manner, with data from each domain being observed in an online fashion. It is a well-known problem that lifelong learning suffers from the catastrophic forgetting (CF). To handle this challenge, existing works have leveraged experience replay or dynamic architecture to consolidate the past knowledge, which however result in incremental memory space or high computational cost. In this work, we propose a novel framework name "power norm based lifelong learning" (PNLLL), which aims to remedy the catastrophic forgetting issues with a power normalization on NLP transformer models. Specifically, PNLLL leverages power norm to achieve a better balance between past experience rehearsal and new knowledge acquisition. These designs enable the knowledge adaptation onto new tasks while memorizing the experience of past tasks. Our experiments on paraphrase generation tasks show that PNLLL not only outperforms SOTA models by a considerable margin and but also largely alleviates forgetting.

Catastrophic forgetting poses a major challenge in continual learning where the old knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to solve this challenge through generative models or exemplar-replay strategies. However, such methods may not alleviate the issue that the low-quality samples are generated or selected for the replay, which would directly reduce the effectiveness of the model, especially in the class imbalance, noise, or redundancy scenarios. Accordingly, how to select a suitable coreset during continual learning becomes significant in such setting. In this work, we propose a novel approach that leverages continual coreset sampling (CCS) to address these challenges. We aim to select the most representative subsets during each iteration. When the model is trained on new tasks, it closely approximates/matches the gradient of both the previous and current tasks with respect to the model parameters. This way, adaptation of the model to new datasets could be more efficient. Furthermore, different from the old data storage for maintaining the old knowledge, our approach choose to preserving them in the latent space. We augment the previous classes in the embedding space as the pseudo sample vectors from the old encoder output, strengthened by the joint training with selected new data. It could avoid data privacy invasions in a real-world application when we update the model. Our experiments validate the effectiveness of our proposed approach over various CV/NLP datasets under against current baselines, and we also indicate the obvious improvement of model adaptation and forgetting reduction in a data-free manner.

Causal effect inference has many applications in data analysis and predictions, e.g., user behavior modeling, medical treatment effect prediction, etc. We introduce a new method to perform causal effect inference using flow-based latent-variable models. Our method leverages the expressive power of flow-based models and tries to recover the complex relationship between observations and unobserved confounders. A methodology has been developed to perform causal effect inference along with theoretical analysis. Experimental studies are presented to verify the proposed approach. Empirical results show that the proposed method outperforms baselines on different datasets.

This paper introduces a novel approach embedding flow-based models in hierarchical structures. The proposed model learns the representation of high-dimensional data via a message-passing scheme by integrating flow-based functions through variational inference. Meanwhile, our model produces a representation of the data using a lower dimension, thus overcoming the drawbacks of many flow-based models, usually requiring a high dimensional latent space involving many trivial variables. With the proposed aggregation nodes, our model provides a new approach for distribution modeling and numerical inference on datasets. Multiple experiments on synthetic and real-world datasets show the benefits of our~proposed~method and potentially broad applications.

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and attributes. In this paper, we consider the following challenging multi-label stream scenario: the new labels emerge continuously in the changing environments, and are assigned to the previous data. In this setting, data mining solutions must be able to learn the new concepts and avoid catastrophic forgetting simultaneously. We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels. We utilize the knowledge from the previous tasks to learn new knowledge to solve the current task. Then, the system compresses historical and novel knowledge and preserves it while waiting for new emerging tasks. CIFDM consists of three components: 1) a knowledge bank that stores the existing feature-level compressed knowledge, and predicts the observed labels so far; 2) a pioneer module that aims to learn and predict new emerged labels based on knowledge bank.; 3) an interactive knowledge compression function which is used to compress and transfer the new knowledge to the bank, and then apply the current compressed knowledge to initialize the label embedding of the pioneer for the next task.

In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, we integrate multi-gate mixture of experts and soft actor critic into the ranking system. We demonstrated that our proposed framework can greatly reduce the loss function compared with systems only based on single strategies.

The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.

Both topic modeling and word embedding map documents onto a low-dimensional space, with the former clustering words into a global topic space and the latter into a local continuous embedding space. In this study, we propose the TMSA framework to unify these two complementary patterns by the construction of a mutual learning mechanism between word-cooccurrence based topic modeling and autoencoder. In our model, word topics generated with topic modeling are passed into auto-encoder to impose topic sparsity so that auto-encoder can learn topic-relevant word representations. In return, word embedding learned by autoencoder is sent back to topic modeling to improve the quality of topic generations. Empirical studies show the effectiveness of the proposed TMSA model in discovering topics and embedding words.

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA-KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low-dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA-KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective Knowledge Embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully-designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely-adopted benchmark demonstrate that the proposed KEQA outperforms the state-of-the-art QA-KG methods.

Domain ontology is widely used to index literature for the convenience of literature retrieval. Due to the high cost of manual curation of key aspects from the scientific literature, automated methods are crucially required to assist the process of semantic indexing. However, it is a challenging task due to the huge amount of terms and complex hierarchical relations involved in a domain ontology. In this paper, in order to lessen the curse of dimensionality and enhance the training efficiency, we propose an approach named Deep Level-wise Extreme Multi-label Learning and Classification (Deep Level-wise XMLC), to facilitate the semantic indexing of literatures. Specifically, Deep Level-wise XMLC is composed of two sequential modules. The first module, deep level-wise multi-label learning, decomposes the terms of a domain ontology into multiple levels and builds a special convolutional neural network for each level with category-dependent dynamic max pooling and macro F-measure based weights tuning. The second module, hierarchical pointer generation model merges the level-wise outputs into a final summarized semantic indexing. We demonstrate the effectiveness of Deep Level-wise XMLC by comparing it with several state-of-the-art methods on automatic labeling of MeSH, on literature from PubMed MEDLINE and automatic labeling of AmazonCat13K.

Deep learning models have achieved great successes these days. There are intensive studies of word representation learning for question classification. As questions are typically short texts, existing techniques are often not effective for extracting discriminative representations of questions just from a limited number of words. This motivates us to exploit additional information beyond words in order to improve the representation learning of questions. On one hand, topic modeling often captures meaningful semantic structures from the question corpus. Such global topical information should be helpful for question representations. On the other hand, entities extracted from question themselves provide more auxiliary information for short texts from a local viewpoint. Together with words, topics and entities, question representations can be substantially improved.In this paper, we propose a unified neural network framework by integrating Topic modeling, Word embedding and Entity Embedding (TWEE) for question representation learning. Concretely, we introduce a novel topic sparse autoencoder to incorporate discriminative topics into the representation learning of questions. In addition, both words and entity related information are embedded into the network to help learn a more comprehensive question representation. Empirical experiments show that the proposed TWEE framework outperforms the state-of-the-art methods on different datasets.