Session:「Chatbot Interfaces」

Typefaces and the Perception of Humanness in Natural Language Chatbots

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025919

論文アブストラクト: How much do visual aspects influence the perception of users about whether they are conversing with a human being or a machine in a mobile-chat environment? This paper describes a study on the influence of typefaces using a blind Turing test-inspired approach. The study consisted of two user experiments. First, three different typefaces (OCR, Georgia, Helvetica) and three neutral dialogues between a human and a financial adviser were shown to participants. The second experiment applied the same study design but OCR font was substituted by Bradley font. For each of our two independent experiments, participants were shown three dialogue transcriptions and three typefaces counterbalanced. For each dialogue typeface pair, participants had to classify adviser conversations as human or chatbot-like. The results showed that machine-like typefaces biased users towards perceiving the adviser as machines but, unexpectedly, handwritten-like typefaces had not the opposite effect. Those effects were, however, influenced by the familiarity of the user to artificial intelligence and other participants' characteristics.

日本語のまとめ:

チャット環境において,相手が人間なのか機械なのかを,視覚的側面が認識に及ぼす影響についての実験.機械的な書体は「相手は機械である」と認識の偏りがあったが,手書き書体の場合では「相手は人間である」という認識の偏りは見られなかった.

"Could You Define That in Bot Terms"?: Requesting, Creating and Using Bots on Reddit

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025830

論文アブストラクト: Bots are estimated to account for well over half of all web traffic, yet they remain an understudied topic in HCI. In this paper we present the findings of an analysis of 2284 submissions across three discussion groups dedicated to the request, creation and discussion of bots on Reddit. We set out to examine the qualities and functionalities of bots and the practical and social challenges surrounding their creation and use. Our findings highlight the prevalence of misunderstandings around the capabilities of bots, misalignments in discourse between novices who request and more expert members who create them, and the prevalence of requests that are deemed to be inappropriate for the Reddit community. In discussing our findings, we suggest future directions for the design and development of tools that support more carefully guided and reflective approaches to bot development for novices, and tools to support exploring the consequences of contextually-inappropriate bot ideas.

日本語のまとめ:

Redditにおけるボットに関する3つのディスカッショングループを調査・分析し,初心者や熟練者が混じっての開発やリクエスト,問題解決の様子から,ボット開発のサポートツール設計開発の方向性や,アイデアの適切な検索をサポートするツールを提案

Response Times when Interpreting Artificial Subtle Expressions are Shorter than with Human-like Speech Sounds

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025649

論文アブストラクト: Artificial subtle expressions (ASEs) are machine-like expressions used to convey a system's confidence level to users intuitively. In this paper, we focus on the cognitive loads of users in interpreting ASEs in this study. Specifically, we assume that a shorter response time indicates less cognitive load, and we hypothesize that users will show a shorter response time when interpreting ASEs compared with speech sounds. We succeeded in verifying our hypothesis in a web-based investigation done to comprehend participants' cognitive loads by measuring their response times in interpreting ASEs and speeches.

日本語のまとめ:

応答時間が短いほど認知負荷が低いと仮定し,信頼性レベルが異なる2種類の音を利用した実験を行い,ASE(「人工物らしい」情報表現)と合成音声によるスピーチを比較したところ,ASEのほうが応答時間が短かった.

A New Chatbot for Customer Service on Social Media

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025496

論文アブストラクト: Users are rapidly turning to social media to request and receive customer service; however, a majority of these requests were not addressed timely or even not addressed at all. To overcome the problem, we create a new conversational system to automatically generate responses for users requests on social media. Our system is integrated with state-of-the-art deep learning techniques and is trained by nearly 1M Twitter conversations between users and agents from over 60 brands. The evaluation reveals that over 40% of the requests are emotional, and the system is about as good as human agents in showing empathy to help users cope with emotional situations. Results also show our system outperforms information retrieval system based on both human judgments and an automatic evaluation metric.

日本語のまとめ:

LSTMなどの深層学習を用いて,ユーザの要求に対する応答を自動生成する新しい会話システムの開発.妥当性・共感性・有用性の3つの観点から評価を行ったところ,すべての側面においてキーワード検索アプローチよりも柔軟性が高く,優れていることがわかった.