اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدEliciting and Analysing Users Envisioned Dialogues with Perfect Voice Assistants
133
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistants role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command Q&As.
قيم البحث
اقرأ أيضاً
A critical goal, is that organizations and citizens can easily access the geographic information required for good governance. However, despite the costly efforts of governments to create and implement Spatial Data Infrastructures (SDIs), this goal i
s far from being achieved. This is partly due to the lack of usability of the geoportals through which the geographic information is accessed. In this position paper, we present IDEAIS, a research network composed of multiple Ibero-American partners to address this usability issue through the use of Intelligent Systems, in particular Smart Voice Assistants, to efficiently recover and access geographic information.
Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other words, interactions have to be initiated by the user, which somewhat
limits their usability and user experience. We propose, that the next generation of such devices should be able to proactively provide the right information in the right way at the right time, without being prompted by the user. However, achieving this is not straightforward, since there is the danger it could interrupt what the user is doing too much, resulting in it being distracting or even annoying. Furthermore, it could unwittingly, reveal sensitive/private information to third parties. In this report, we discuss the challenges of developing proactively initiated interactions, and suggest a framework for when it is appropriate for the device to intervene. To validate our design assumptions, we describe firstly, how we built a functioning prototype and secondly, a user study that was conducted to assess users reactions and reflections when in the presence of a proactive voice assistant. This pre-print summarises the state, ideas and progress towards a proactive device as of autumn 2018.
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that
jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.
AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers goals and then help them wi
thout being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers goals, reasoning, and capabilities.
Virtual assistants such as Amazons Alexa, Apples Siri, Google Home, and Microsofts Cortana, are becoming ubiquitous in our daily lives and successfully help users in various daily tasks, such as making phone calls or playing music. Yet, they still st
ruggle with playful utterances, which are not meant to be interpreted literally. Examples include jokes or absurd requests or questions such as, Are you afraid of the dark?, Who let the dogs out?, or Order a zillion gummy bears. Today, virtual assistants often return irrelevant answers to such utterances, except for hard-coded ones addressed by canned replies. To address the challenge of automatically detecting playful utterances, we first characterize the different types of playful human-virtual assistant interaction. We introduce a taxonomy of playful requests rooted in theories of humor and refined by analyzing real-world traffic from Alexa. We then focus on one node, personification, where users refer to the virtual assistant as a person (What do you do for fun?). Our conjecture is that understanding such utterances will improve user experience with virtual assistants. We conducted a Wizard-of-Oz user study and showed that endowing virtual assistant s with the ability to identify humorous opportunities indeed has the potential to increase user satisfaction. We hope this work will contribute to the understanding of the landscape of the problem and inspire novel ideas and techniques towards the vision of giving virtual assistants a sense of humor.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
