Titlebook: Natural Language Processing and Chinese Computing; 8th CCF Internationa Jie Tang,Min-Yen Kan,Hongying Zan Conference proceedings 2019 Sprin

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https://doi.org/10.1007/978-3-030-32233-5artificial intelligence; classification; computational linguistics; data mining; databases; fuzzy systems

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Improving Question Answering by Commonsense-Based Pre-trainingsense knowledge. We believe the main reason is the lack of commonsense connections between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts,

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Multi-strategies Method for Cold-Start Stage Question Matching of rQA Tasksource limitation of many real applications, even the best SSEI models may underperform. To enhance the performance, this paper firstly proposes a novel deep neural network named Densely-connected Fusion Attentive Network (DFAN). The key idea behind our model is to learn the interactive semantic inf

貞潔

Multilingual Dialogue Generation with Shared-Private Memorymultilingual dialogue system. Specifically, we augment the sequence to sequence framework with improved shared-private memory. The shared memory learns common features among different languages and facilitates a cross-lingual transfer to boost dialogue systems, while the private memory is owned by e

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How Question Generation Can Help Question Answering over Knowledge Baseer. The task of question generation (QG) is to generate a corresponding natural language question given the input answer, while question answering (QA) is a reverse task to find a proper answer given the question. For the KBQA task, the answer could be regarded as a fact containing a predicate and t