ABSTRACT Domain adaptation aims to transfer knowledge from the labeled source domain to an unlabeled target domain that follows a similar but different distribution. Recently, adversarial-based methods have achieved remarkable success due to the excellent performance of domain-invariant feature presentation learning. However, the adversarial methods learn the transferability at the expense of the discriminability in feature representation, leading to low generalization to the target domain. To this end, we propose a Multi-view Feature Learning method for the Over-penalty in Adversarial Domain Adaptation. Specifically, multi-view representation learning is proposed to enrich the discriminative information contained in domain-invariant feature representation, which will counter the over-penalty for discriminability in adversarial training. Besides, the class distribution in the intra-domain is proposed to replace that in the inter-domain to capture more discriminative information in the learning of transferrable features. Extensive experiments show that our method can improve the discriminability while maintaining transferability and exceeds the most advanced methods in the domain adaptation benchmark datasets.

Knowledge graph (KG) has played an important role in enhancing the performance of many intelligent systems. In this paper, we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications. Unlike previous works that build knowledge graph with graph databases, we build the knowledge graph on top of SogouQdb, a distributed search engine developed by Sogou Web Search Department, which can be easily scaled to support petabytes of data. As a supplement to the search engine, we also introduce a series of models to support inference and graph based querying. Currently, the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity links. We also introduce three applications of knowledge graph in Sogou Inc.: entity detection and linking, knowledge based question answering and knowledge based dialog system. These applications have been used in Web search products to help user acquire information more efficiently.