An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.     

新时期我国软科学研究前沿热点研究 ——基于软科学文献的知识图谱分析（2008-2018）

运用科学计量方法对软科学领域的六大核心期刊(2008—2018年)现状和研究热点进行分析。研究发现,目前我国软科学以国家创新驱动发展战略为主要研究方向,围绕区域创新体系及创新链的热点焦点问题和关键环节开展研究,同时,发现各个参与研究者之间合作紧密。研究运用描述性分析的方法,管窥我国软科学领域研究现状,并对未来发展做出展望,为我国软科学繁荣发展提供参考。     

Zero-shot cross-lingual transfer language selection using linguistic similarity

We study the selection of transfer languages for different Natural Language Processing tasks, specifically sentiment analysis, named entity recognition and dependency parsing. In order to select an optimal transfer language, we propose to utilize different linguistic similarity metrics to measure the distance between languages and make the choice of transfer language based on this information instead of relying on intuition. We demonstrate that linguistic similarity correlates with cross-lingual transfer performance for all of the proposed tasks. We also show that there is a statistically significant difference in choosing the optimal language as the transfer source instead of English. This allows us to select a more suitable transfer language which can be used to better leverage knowledge from high-resource languages in order to improve the performance of language applications lacking data. For the study, we used datasets from eight different languages from three language families.     

Improving topic disentanglement via contrastive learning

With the emergence and development of deep generative models, such as the variational auto-encoders (VAEs), the research on topic modeling successfully extends to a new area: neural topic modeling, which aims to learn disentangled topics to understand the data better. However, the original VAE framework had been shown to be limited in disentanglement performance, bringing their inherent defects to a neural topic model (NTM). In this paper, we put forward that the optimization objectives of contrastive learning are consistent with two important goals (alignment and uniformity) of well-disentangled topic learning. Also, the optimization objectives of contrastive learning are consistent with two key evaluation measures for topic models, topic coherence and topic diversity. So, we come to the important conclusion that alignment and uniformity of disentangled topic learning can be quantified with topic coherence and topic diversity. Accordingly, we are inspired to propose the Contrastive Disentangled Neural Topic Model (CNTM). By representing both words and topics as low-dimensional vectors in the same embedding space, we apply contrastive learning to neural topic modeling to produce factorized and disentangled topics in an interpretable manner. We compare our proposed CNTM with strong baseline models on widely-used metrics. Our model achieves the best topic coherence scores under the most general evaluation setting (100% proportion topic selected) with 25.0%, 10.9%, 24.6%, and 51.3% improvements above the second-best models’ scores reported on four datasets of 20 Newsgroups, Web Snippets, Tag My News, and Reuters, respectively. Our method also gets the second-best topic diversity scores on the dataset of 20Newsgroups and Web Snippets. Our experimental results show that CNTM can effectively leverage the disentanglement ability from contrastive learning to solve the inherent defect of neural topic modeling and obtain better topic quality.     

Neural entity alignment with cross-modal supervision

The majority of currently available entity alignment (EA) solutions primarily rely on structural information to align entities, which is biased and disregards additional multi-source information. To compensate for inadequate structural details, this article suggests the SKEA framework, which is a simple but flexible framework for Entity Alignment with cross-modal supervision of Supporting Knowledge. We employ a relational aggregate network to specifically utilize the details about the entity and its neighbors. To overcome the limitations of relational features, two multi-modal encode modules are being used to extract visual and textural information. A new set of potential aligned entity pairs are generated by SKEA in each iteration using the knowledge of two reference modalities, which can enhance the model’s supervision. It is important to note that the supporting information used in our framework does not participate in the network’s backpropagation, which considerably improves efficiency and differs dramatically from earlier work. In comparison to existing baselines, experiments demonstrate that our proposed framework can incorporate multi-aspect information efficiently and enable supervisory signals from other modalities to transmit to entities. The maximum performance improvement of 5.24% indicates our suggested framework’s superiority, especially for sparse KGs.     

基于主题因素分析的图书情报领域零被引现象研究

【目的】 通过分析国内图书情报领域零被引文章的主题特征,为图书情报领域科研工作者的写作以及科技期刊的选题、出版等提供参考和建议。【方法】 以17种《中文社会科学引文索引(2017—2018)》来源期刊为数据来源,利用知识图谱,展现其2008—2012年零被引论文的主题特征,通过对比同期高被引论文的主题,总结图书情报领域出现零被引现象的主题因素。【结果】 与高被引论文相比,零被引论文的研究主题分散、陈旧。但是研究主题并没有显示出偏离学科研究领域或者高度前瞻性的特点。【结论】 建议科研工作者在扎实写作的同时,注意创新研究主题,建议科技期刊选题策划应平衡各项原则,避免一味地追求创新。