Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System最新文献_第4页

Joint Slot Filling and Intent Detection in Spoken Language Understanding by Hybrid CNN-LSTM Model 基于CNN-LSTM混合模型的口语理解联合槽填充和意图检测

Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System Pub Date : 2020-10-27 DOI: 10.1145/3437802.3437822

Moath Al Ali, Bassel Zaity, P. Drobintsev, H. Wannous, Igor Chernoruckiy, A. Filchenkov

{"title":"Joint Slot Filling and Intent Detection in Spoken Language Understanding by Hybrid CNN-LSTM Model","authors":"Moath Al Ali, Bassel Zaity, P. Drobintsev, H. Wannous, Igor Chernoruckiy, A. Filchenkov","doi":"10.1145/3437802.3437822","DOIUrl":"https://doi.org/10.1145/3437802.3437822","url":null,"abstract":"We investigate the usage of hybrid convolutional and long- short-term memory neural networks for joint slot filling and intent detection in spoken language understanding. We propose a novel model that combines between convolutional neural networks, for their ability to detect complex features in the input sequences by applying filters to frames of these inputs, and recurrent neural networks taking in account the fact, that they can keep track of the long- and short- term dependencies in the input sequences. We choose to build a model for joint slot filling and intent detection, because we believe, that there is a strong relation between the intent and the semantic slots. A joint model can reflect this relation, figure it out and make use of it to enhance the prediction results. We use the Airline Travel Information System (ATIS) dataset to measure the performance of our model and compare it with the results of other models, as this dataset has become one of the most popular datasets for spoken language understanding problem.","PeriodicalId":429866,"journal":{"name":"Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133374702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

On the Auto-Tuning of Elastic-search based on Machine Learning 基于机器学习的弹性搜索自调优研究

Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System Pub Date : 2020-10-27 DOI: 10.1145/3437802.3437828

Zhenyan Lu, Chao Chen, Jinhan Xin, Zhibin Yu

{"title":"On the Auto-Tuning of Elastic-search based on Machine Learning","authors":"Zhenyan Lu, Chao Chen, Jinhan Xin, Zhibin Yu","doi":"10.1145/3437802.3437828","DOIUrl":"https://doi.org/10.1145/3437802.3437828","url":null,"abstract":"Elastic-search is a distributed search engine which is used to process large amount of data widely. It has a vast number of configuration parameters which are extremely difficult to manually tune to achieve optimal throughput and latency. This paper presents an auto-tuning method to improve the performance of Elastic-search based on random forest and gradient boosting regression trees. By analyzing the working process of Elastic-search, performance-sensitive configuration parameters are selected to establish a machine learning model with high accuracy, so as to accurately predict the performance of Elastic-search with different configurations. With the help of performance prediction, the genetic algorithm finds the optimal configuration of Elastic-search under given system conditions. Three data sets with different sizes and structures are selected for evaluation and the benchmarking tool EsRally tests the performance of index and query operation. Experimental results show that our proposed method can improve the performance by 2.73 times on average and up to 7.02 times compared to the default configuration of Elastic-search.","PeriodicalId":429866,"journal":{"name":"Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121441958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Research on Text Error Correction Algorithm after Automatic Speech Recognition Based on Pragmatic Information 基于语用信息的语音自动识别后文本纠错算法研究

Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System Pub Date : 2020-10-27 DOI: 10.1145/3437802.3437830

Yiming Y. Sun, Tianyu Xiao, Chen Yang, Wei Liu

引用次数: 1

Generative Adversarial Networks for Respiratory Sound Augmentation 呼吸声增强的生成对抗网络

Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System Pub Date : 2020-10-27 DOI: 10.1145/3437802.3437821

Kirill Kochetov, A. Filchenkov

引用次数: 4

Learning Long-text Semantic Similarity with Multi-Granularity Semantic Embedding Based on Knowledge Enhancement 基于知识增强的多粒度语义嵌入学习长文语义相似度

Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System Pub Date : 2020-04-13 DOI: 10.1145/3437802.3437806

Deguang Peng, Bohui Hao, Xianlun Tang, Yingjie Chen, Jian Sun, Runzhu Wang

引用次数: 1