Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user's mood. Its novelty lies in the concept of "intelligent radio", according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -- background news -- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.

翻译：当前的语言处理技术已能构建对话式聊天机器人平台。尽管人工智能在许多大众市场领域仍不足以支撑令人满意的用户体验，但对话界面已进入呼叫中心和在线购物助手等特定应用场景。然而，这些技术迄今尚未应用于尤其易受数字鸿沟影响的老年人群体的社会包容问题。许多老年人通过电视和广播等传统媒介缓解孤独感，这些媒介能产生陪伴感。本文提出了EBER聊天机器人，旨在缩小老年人的数字鸿沟。EBER在后台读取新闻，并根据用户情绪调整回复。其创新之处在于"智能广播"概念：不通过简化数字信息系统来适应老年人，而是以他们熟悉的传统渠道（背景新闻播报）为基础，通过语音对话交互进行功能增强。我们通过结合人工智能建模语言、自动自然语言生成和情感分析技术实现了这一目标。该系统通过提取用户对聊天机器人问题的回答中的词语，与新闻条目中的关键词进行匹配，从而获取感兴趣的数字内容。这种方法可根据词语空间的空间表征定义用户抽象能力指标。为验证方案的有效性，我们展示了与老年人进行的真实实验结果，这些实验提供了宝贵的见解。我们的方法在测试中表现令人满意，并提升了参与者的信息检索能力。