Recently, there has been a growing interest in constructing deep learning schemes for Low-Light Vision (LLV). Existing techniques primarily focus on designing task-specific and data-dependent vision models on the standard RGB domain, which inherently contain latent data associations. In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain. This novel approach connects diverse vision problems by explicitly depicting data generation, which is the first in the field. To precisely characterize the latent correspondence between the generative procedure and the vision task, we establish a bilevel model with the parameters of the generative block defined as the upper level and the parameters of the vision task defined as the lower level. We further develop two types of learning strategies targeting different goals, namely low cost and high accuracy, to acquire a new bilevel generative learning paradigm. The generative blocks embrace a strong generalization ability in other low-light vision tasks through the bilevel optimization on enhancement tasks. Extensive experimental evaluations on three representative low-light vision tasks, namely enhancement, detection, and segmentation, fully demonstrate the superiority of our proposed approach. The code will be available at https://github.com/Yingchi1998/BGL.

Millions of patent applications are submitted every year. Patent examiners spend tremendous amount of time to evaluate the quality of them for approval or denial. A system that can automatically evaluate patents and expedite the evaluation process is much needed. This kind of tool can also help the small enterprises, patent attorneys and agents in preparing their patent applications. This study proposes a model that can rate the quality of a patent by utilizing both the semantic and novelty information of the patent. We also built a dataset of more than 32,000 Chinese patents with manual ratings from professional patent examiners. Our experiments show that the proposed model outperforms other approaches.

In this paper, we present a large Chinese news article dataset with 4.4 million articles. These articles are obtained from different news channels and sources. They are labeled with multi-level topic categories, and some of them also have summaries. This is the first Chinese news dataset that has both hierarchical topic labels and article full texts. And it is also the largest Chinese news topic dataset. We describe the data collection, annotation and quality evaluation process. The basic statistics of the dataset, comparison with other datasets and benchmark experiments are also presented.

Millions of trademarks were registered last year in China, and thousands of applications are submitted daily. A trademark must be unique in the category it belongs to. Therefore, each new trademark application needs to be checked against all the existing ones in its category. A trademark can be a text string (characters, words or phrases), a figure (symbol or design), or both. In this study, we focus on the textual trademark in Chinese, and propose a model for finding similar trademarks for a given one. This neural network model exploits the semantic, phonetic and visual similarities between two textual trademarks. We evaluated our model based on a dataset that were built from the real trademark application data. Our evaluation shows that the proposed model outperforms other approaches.

Recently more and more personal stories about sexual harassment are shared online, mainly inspired by the \#MeToo movement. Safecity is an online forum for victims of sexual harassment to share their personal experience. Previous study applied neural network models to classify the harassment forms of the stories. To uncover patterns of sexual harassment, the extraction of the key elements and the categorization of these stories in different dimensions can be useful as well. In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words. In addition, we categorized these stories from different dimensions, such as location, time, and harassers' characteristics, including their age range, single/multiple harassers, profession, and relationship with the victims. We further demonstrated that encoding the key element information in the story categorization model can improve its performance. The proposed approaches and analysis would be helpful in automatically filing reports, raising public awareness, making preventing strategies and etc.

Yingchi Liu, Quanzhi Li, Marika Cifor, Xiaozhong Liu, Qiong Zhang, Luo Si. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Event trigger detection remains a challenging task. Most of previous studies focused on variations of model structures to extract features from the local context of the trigger words. However, few studies focused on the utilization of document level information. In this work, we studied the benefit of exploiting the document level information for event trigger detections in textual data. Two approaches of extracting document features are proposed, and the document features are integrated with the embeddings generated from the local context of the trigger word using a convolutional neural network (CNN) model. Our experiment shows that these two methods both outperform the CNN-based baseline model.