The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the e...
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The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the existing knowledge graph still has some problems which form hot research topics in recent years. Numerous methods have been proposed based on various representation techniques. Graph Neural Network, a framework that uses deep learning to process graph-structured data directly, has significantly advanced the state-of-the-art in the past few years. This study firstly is aimed at providing a broad, complete as well as comprehensive overview of GNN-based technologies for solving four different KG tasks, including link prediction, knowledge graph alignment, knowledge graph reasoning, and node classification. Further, we also investigated the related artificial intelligence applications of knowledge graphs based on advanced GNN methods, such as recommender systems, question answering, and drug-drug interaction. This review will provide new insights for further study of KG and GNN.
As an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing. Existing works in this field mainly focus on consistency of ne...
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As an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing. Existing works in this field mainly focus on consistency of neural response generation whilst ignoring the effect of emotion state on the response generation. In this paper, we propose an Emotion-based natural language Responding Model (ERM) to address the challenging issue in conversation. ERM encodes the emotion state of the conversation as distributed embedding into the process of response generation, redefines an objective function that jointly trains our model and introduces a novel re-rank function to select the appropriate response. Experimental results on Chinese conversation dataset show that our method yields qualitative performance improvements in the Perplexity (PPL), Word Error-rate (WER) and Bilingual Evaluation Understudy (BLEU) compared with the baseline sequence-to-sequence (Seq2Seq) model, and achieves better performance than the state-of-the-art in terms of emotion and content consistency of the response.
Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat ...
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ISBN:
(纸本)9781450362016
Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart replies are dynamically selected according to conversation content using machine learning algorithms. Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply. It is designed specifically for mobile applications with short and non-canonical messages. Reply retrieval utilizes pairings between intent and reply based on their popularity in chat messages as derived from historical data. For intent detection, a set of embedding and classification techniques are experimented with, and we choose to deploy a solution using unsupervised distributed embedding and nearest-neighbor classifier. It has the advantage of only requiring a small amount of labeled training data, simplicity in developing and deploying to production, and fast inference during serving and hence highly scalable. At the same time, it performs comparably with deep learning architectures such as word-level convolutional neural network. Overall, the system achieves a high accuracy of 76% on intent detection. Currently, the system is deployed in production for English-speaking countries and 71% of in-app communications between riders and driver-partners adopted the smart replies to speedup the communication process.
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