Structure-sensitive semantic matching for aggregate question answering over knowledge base

Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets.     

Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

Assessing students' conceptual understanding using an online three‐tier diagnostic test

When solving a mathematical problem, students who do not have sufficient conceptual understanding may perform poorly and exhibit misconceptions. This study was aimed to examine students' conceptual understanding and significant misconceptions when solving number sense‐related problems. An online three‐tier diagnostic test was administered to 125 fifth‐grade students with varied socio‐economic backgrounds in Hong Kong. Only 14.40% of the students exhibited high performance with high confidence, indicating that these students had a profound conceptual understanding of number sense. In addition, the majority of the students (66.40%) did not demonstrate number sense; these students exhibited several significant misconceptions and could solve the questions only by using a rule‐based method or guessing. Accordingly, most students performed unsatisfactorily on number sense‐related problems. This study is imperative in identifying early predictors and provides information for further compatible interventions in the teaching and learning of number sense in Hong Kong in particular and worldwide in general.     

MOBSY: Integration of vision and dialogue in service robots

Abstract. This contribution introduces MOBSY, a fully integrated, autonomous mobile service robot system. It acts as an automatic dialogue-based receptionist for visitors to our institute. MOBSY incorporates many techniques from different research areas into one working stand-alone system. The techniques involved range from computer vision over speech understanding to classical robotics. Along with the two main aspects of vision and speech, we also focus on the integration aspect, both on the methodological and on the technical level. We describe the task and the techniques involved. Finally, we discuss the experiences that we gained with MOBSY during a live performance at our institute.     

An experiment for showing some kind of artificial understanding

In this paper we design an experiment which can be depicted as a simple scenario, a very limited 'world'. In this world, there are an actor that can pursue a project and an observer that is keeping its eyes on the actor. We try to show in the experiment that the observer can to some degree understand the actor based on its knowledge and some metaphors, i.e. understand what the actor is doing and why. As the conclusion of this experiment, we try to show some features of 'understanding'. These are (1) that 'understanding' has to be based on some preliminary knowledge; (2) that 'understanding' is a process of incremental learning; (3) that, as for symbolic systems, some metaphors are necessary for mapping real entities into concepts in mind.     

信息理解系统的结构及理解过程

主要讨论消息理解的基本模式及其信息理解系统，并且讨论了二类消息的语义理解例以及理解客观度．     

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本文根据四川天然气产能建设多年的实践,分析了产能建设的特点和存在问题,着重强调了当前搞好产能建设应注意的几个问题,供决策和工作参考。     

计算机基础课题库系统中试卷生成模型研究

探讨了计算机基础课程题库系统中题型难度系数确定模型、试题在章节中的分布模型、各级难度题型分布模型，认为利用这些模型和随机函数能够确定计算机基础课程相应章节中相应等级难度的试题中抽取相应数量的试题，再利用数据库的检索功能实现试卷的自动生成。     

隧道工程施工中应注意的问题

通过对海南省东线高速公路大茅山隧道施工的全程监理,阐明了在施工中应重点注意的几个问题,在此基础上,总结出隧道工程质量监督中必须做到的几点,以确保隧道工程施工安全及工程质量.