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2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)最新文献

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Automatic Speech Recognition: Do Emotions Matter? 自动语音识别:情绪重要吗?
2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE) Pub Date : 2019-12-01 DOI: 10.1109/CDKE46621.2019.00009
Fabio Catania, Pietro Crovari, Micol Spitale, F. Garzotto
{"title":"Automatic Speech Recognition: Do Emotions Matter?","authors":"Fabio Catania, Pietro Crovari, Micol Spitale, F. Garzotto","doi":"10.1109/CDKE46621.2019.00009","DOIUrl":"https://doi.org/10.1109/CDKE46621.2019.00009","url":null,"abstract":"By leveraging the advancements in Natural Language Processing and Cognitive Computing, conversational artificial intelligence (AI) has become more mature over the last years. It serves humans in a broad range of applications in business enterprises, government, health-care, and entertaining, and it is getting more embedded into peoples' lives. However, despite the recent improvements, we are still far away from a robust or general AI comparable to human intelligence, especially when it comes to adaptive intelligence able to settle into non-standard and noisy environments. In this paper, we bring to light that emotion in speech negatively affects automatic speech recognition and automatic human inputs understanding. Therefore, emotion is to be considered as a noise compromising the understanding of what the user says and consequently messing up the whole interaction with conversational technologies. For this study, Google Cloud Speech-to-Text and IBM Watson Speech-to-Text have been used.","PeriodicalId":341829,"journal":{"name":"2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132914054","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: 4
Dialogue Generation Using Self-Attention Generative Adversarial Network 使用自注意生成对抗网络的对话生成
2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE) Pub Date : 2019-12-01 DOI: 10.1109/CDKE46621.2019.00012
Amartya Hatua, Trung-Thanh Nguyen, A. Sung
{"title":"Dialogue Generation Using Self-Attention Generative Adversarial Network","authors":"Amartya Hatua, Trung-Thanh Nguyen, A. Sung","doi":"10.1109/CDKE46621.2019.00012","DOIUrl":"https://doi.org/10.1109/CDKE46621.2019.00012","url":null,"abstract":"Developing an intelligent conversation system is one of the longest-running goals in Artificial Intelligence research. For a successful conversation system, appropriate dialogue generation is one of the main components. This article describes a method to generate dialogue using Self-Attention Generative Adversarial Network (SAGAN). The general objective of this method is to produce dialogue which is very similar to human-generated dialogue both structurally and contextually. Generative Adversarial Network (GAN) is mainly used to generate dialogue which is structurally very similar to human-generated dialogue. On the other hand, the self-attention network helps to maintain the context of the conversation in extreme detail for both single track and multi-track dialogue generation. SAGAN enhances the accuracy for both single track and multi-track dialogue generation in a significant way.","PeriodicalId":341829,"journal":{"name":"2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)","volume":"os-23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127769764","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: 7
Approaching Natural Conversation Chatbots by Interactive Dialogue Modelling & Microsoft LUIS 通过交互式对话建模和Microsoft LUIS接近自然对话聊天机器人
2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE) Pub Date : 2019-12-01 DOI: 10.1109/CDKE46621.2019.00013
Alexander Wachtel, Dominik Fuchß, Sophie Schulz, W. Tichy
{"title":"Approaching Natural Conversation Chatbots by Interactive Dialogue Modelling & Microsoft LUIS","authors":"Alexander Wachtel, Dominik Fuchß, Sophie Schulz, W. Tichy","doi":"10.1109/CDKE46621.2019.00013","DOIUrl":"https://doi.org/10.1109/CDKE46621.2019.00013","url":null,"abstract":"In this paper, we preset guidelines on how to model a dialogue-based conversation with a chatbot build on Microsoft conversational services. Therefore, we created a free framework which manages dialogues and user sessions. For this purpose, we use a systematic classification of the input and intents. Especially, we used Microsoft Language Understanding Intelligent Service (LUIS) for intent extraction. LUIS provides a platform to extract the user need by calculating the intent probability. Unfortunately, LUIS does not provide dialogue management. For that reason, developers need to create own dialogue behavior in Visual Studio project. Therefore, we extracted and use it as a free guideline for the community and other platform developers.","PeriodicalId":341829,"journal":{"name":"2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130153139","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
Towards a Bidirectional Machine Translator Generator for Multilingual Communication 面向多语言交流的双向机器翻译生成器
2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE) Pub Date : 2019-12-01 DOI: 10.1109/CDKE46621.2019.00011
Ryan Lane, A. Bansal
{"title":"Towards a Bidirectional Machine Translator Generator for Multilingual Communication","authors":"Ryan Lane, A. Bansal","doi":"10.1109/CDKE46621.2019.00011","DOIUrl":"https://doi.org/10.1109/CDKE46621.2019.00011","url":null,"abstract":"Machine Translation (MT) systems are typically quite complex, especially those used in production environments where high-quality conversational text or speech translation from one language to another is important. As a result, the vast majority of MT systems support translation between a single language pair, often uni-directionally. This research study extends previous work to assess the efficacy of developing a bidirectional translator generator in Prolog programming language using Lexical Functional Grammars. The main research objective is building a machine translator generator for multilingual communication, i.e. developing a system whose inputs are linguistic descriptions of a desired source and target language and whose output is a program that translates between the two natural languages. The implementation of a bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed and assessed in terms of four general classes of translation. The benefits and drawbacks of this approach as generalized to MT systems are also discussed, along with possible areas of future work.","PeriodicalId":341829,"journal":{"name":"2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197473","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: 0
Improving the Quality of the Conversational Datasets through Extensive Semantic Analysis 通过广泛的语义分析提高会话数据集的质量
2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE) Pub Date : 2019-12-01 DOI: 10.1109/CDKE46621.2019.00008
Maria Krommyda, Verena Kantere
{"title":"Improving the Quality of the Conversational Datasets through Extensive Semantic Analysis","authors":"Maria Krommyda, Verena Kantere","doi":"10.1109/CDKE46621.2019.00008","DOIUrl":"https://doi.org/10.1109/CDKE46621.2019.00008","url":null,"abstract":"As more and more datasets become available their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task-and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.","PeriodicalId":341829,"journal":{"name":"2019 IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123038343","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
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