【论文推荐】最新七篇聊天机器人相关论文—社交媒体、远程医疗、序列理解、食物推荐、知识学习引擎、终生交互知识学习、任务管理- 专知

【论文推荐】最新七篇聊天机器人相关论文—社交媒体、远程医疗、序列理解、食物推荐、知识学习引擎、终生交互知识学习、任务管理

【导读】专知内容组整理了最近七篇聊天机器人（Chatbot）相关文章，为大家进行介绍，欢迎查看!

1.Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media（触动你的心:一个在社交媒体上为客户服务的音调感知聊天机器人）

作者：Tianran Hu,Anbang Xu,Zhe Liu,Quanzeng You,Yufan Guo,Vibha Sinha,Jiebo Luo,Rama Akkiraju

机构：University of Rochester

摘要：Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.

期刊：arXiv, 2018年3月15日

网址：

http://www.zhuanzhi.ai/document/da90693f9cec89c6cd9cfacc8587c15a

2.Beyond Patient Monitoring: Conversational Agents Role in Telemedicine & Healthcare Support For Home-Living Elderly Individuals（远程医疗和医疗支持中的对话代理对居家老人的支持）

作者：Ahmed Fadhil

摘要：There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition.

期刊：arXiv, 2018年3月3日

网址：

http://www.zhuanzhi.ai/document/6d815f6f819120b6464c28dc3e9da5ce

3.DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks（DeepProbe:通过递归神经网络对信息进行序列理解和聊天机器人设计）

作者：Zi Yin,Keng-hao Chang,Ruofei Zhang

机构：Stanford University

摘要：Information extraction and user intention identification are central topics in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes decisions based on a derived uncertainty (entropy) measure conditioned on user inputs, possibly with multiple rounds of interactions. Three applications, namely a rewritter, a relevance scorer and a chatbot for ad recommendation, were built around DeepProbe, with the first two serving as precursory building blocks for the third. We first use the seq2seq model in DeepProbe to rewrite a user query into one of standard query form, which is submitted to an ordinary recommendation system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance scoring. Finally, we build a chatbot prototype capable of making active user interactions, which can ask questions that maximize information gain, allowing for a more efficient user intention idenfication process. We evaluate first two applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge evaluation. Both demonstrate significant improvements compared with current state-of-the-art systems, proving their values as useful tools on their own, and at the same time laying a good foundation for the ongoing chatbot application.

期刊：arXiv, 2018年3月2日

网址：

http://www.zhuanzhi.ai/document/92aa72b24c3a8b2ce47ba6fe3a64c101

4.Can a Chatbot Determine My Diet?: Addressing Challenges of Chatbot Application for Meal Recommendation（解决聊天机器人应用程序对食物推荐的挑战）

作者：Ahmed Fadhil

摘要：Poor nutrition can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. A conversational agent can support people as a virtual coach, however building such systems still have its associated challenges and limitations. This paper describes the background and motivation for chatbot systems in the context of healthy nutrition recommendation. We discuss current challenges associated with chatbot application, we tackled technical, theoretical, behavioural, and social aspects of the challenges. We then propose a pipeline to be used as guidelines by developers to implement theoretically and technically robust chatbot systems.

期刊：arXiv, 2018年2月26日

网址：

http://www.zhuanzhi.ai/document/ff13c5a7d744f7a952fa2afa59cc8ca5

5.Towards a Continuous Knowledge Learning Engine for Chatbots（为聊天机器人提供持续知识学习引擎）

作者：Sahisnu Mazumder,Nianzu Ma,Bing Liu

摘要：Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this paper, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it. LiLi works by imitating how humans acquire knowledge and perform inference during an interactive conversation. Our experimental results show LiLi is highly promising.

期刊：arXiv, 2018年2月24日

网址：

http://www.zhuanzhi.ai/document/8c13c8eb7a6f5d8630b455c48394a1ac

6.Towards an Engine for Lifelong Interactive Knowledge Learning in Human-Machine Conversations（基于终生交互知识学习的人机对话引擎）

作者：Sahisnu Mazumder,Nianzu Ma,Bing Liu

机构：University of Illinois at Chicago

期刊：arXiv, 2018年2月17日

网址：

http://www.zhuanzhi.ai/document/38b5c03b6549c33eb6d1a6d745b5708b

7.Understanding Chatbot-mediated Task Management（理解Chatbot-mediated任务管理）

作者：Carlos Toxtli,Andrés Monroy-Hernández,Justin Cranshaw

机构：West Virginia University

摘要：Effective task management is essential to successful team collaboration. While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat. Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency. To address this problem, we explore how bots might be used to mediate task management for individuals and teams. We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel. We derived seven insights for the design of future bots for coordinating work.