Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Using a Multiplatform Chatbot as an Online Tutor in a University Course

Abstract: Allowing students to ask questions in a university course is a crucial aspect of learning, which leads to increased learning effectiveness but also increased workload of the teaching staff. To reduce their workload, this paper presents the design of a chatbot for instantly answering students' questions on multiple common social platforms including Telegram, Facebook Messenger and Line. The chatbot can answer questions in natural language and commands. Once the teachers upload the necessary course-related information to an online database, the chatbot can answer questions on the course materials and course logistics (e.g., class schedule). The chatbot also supports a login system so as to provide answers according to different student profiles (e.g., schedule of their enrolled class and score dissemination). A survey on ten undergraduate computer science students showed that our chatbot can effectively act as an online tutor to reduce teachers' workload and will be a useful tool to be integrated into other e-learning platforms.

Context-aware chatbot system and method

Abstract: A context-aware chatbot method and system are provided. The context-aware chatbot method comprises receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user. The context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors.

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

Abstract: Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., “Maybe, I don’t know.” Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills

Abstract: Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

MovieChats: Chat like Humans in a Closed Domain

Abstract: Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work