Semantic parsing is a mainstream method of knowledge base question answering task that first generates a set of logical forms according to question and knowledge base (KB), and then selects the most matching one to get answers. However, existing selection methods are usually based on word-level matching, which cannot capture the structural information or solve the long-term dependency problem of entities. To solve this problem, we propose a syntax-based graph matching method, which explicitly models both question and logical form as graphs, and performs matching at both word-level and structure-level. The multi-level matching strategy not only captures the structural and semantic relations between entities but also explores their intrinsic relations. Such a design can greatly minimize the gap between question and most relevant knowledge from KB, and hence can reason more accurately. We conduct extensive experiments on several benchmarks and demonstrate the effectiveness of our proposed method.

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.

As a fundamental task in NLP, recognizing implicit discourse relations remains a challenging problem for years. One of the most important reasons is the limited amounts of annotated data. On one hand, most existing methods use multi-task methods to enlarge data. External datasets are often fully introduced to the training process which may lead to a negative transfer of noisy data. On the other hand, some previous researches on selecting data mostly focus on designing rules with heuristic methods. It should be difficult to cover all aspects of good samples. Another drawback is that data selection can hardly generally fit well in any other recognition model since it was specialized with a specific discourse relation classifier.In this paper, we propose a novel selective data enhanced model (SDE) based on reinforcement learning. Our model is a general framework, composed of two parts: 1) Discourse relation classifier is designed to identify relations, including multi-level representation module and relation recognition module. 2) Pseudo labeled data selector is designed to pick out data that can enhance the discourse relation classifier. We conduct joint learning alternately to optimize both of the classifier and the selector. Our model is able to expand data selectively. And classifier part can be replaced with any other complicated networks. To further exploit interact signals between arguments, we also present a multi-level representation based on BERT. Experiments show that our model achieves better performance than state-of-the-art methods.