EAGER: Collaborative Research: Interactive Dialog Agents for Social Language Development and Listening Comprehension

练习口头语言技能有助于孩子日后处理书面文字的能力。社会经济地位低的儿童接受的家庭语言输入质量较低，这对语言发展产生了负面影响。许多孩子在开始接受正规教育时，已经在阅读理解方面存在严重缺陷，这是一个他们可能永远无法克服的缺陷。EARLY探索性研究资助（EAGER）项目将研究如何通过教学学习代理，动画故事角色和儿童般的叙述者在讲故事时与孩子进行对话来改善儿童的家庭语言环境。故事人物将直接与孩子交谈，从不同的角度讲述故事，问孩子问题，使用不同的词汇以不同的方式表达这些问题，从而让孩子理解故事，提高他们的社交语言和听力理解能力。 教学式学习代理是一种廉价且易于部署的技术，可以随时提供给任何社会经济群体的儿童。该项目将开发用于表达性交互式讲故事代理的技术，该技术模拟具有高水平社会参与的人类会话讲故事。这将需要使用语音识别和文本到语音输出来有效地管理与孩子的口语对话的方法。该项目将从叙事的深层计算表示中构建，围绕同一个故事生成不同的对话。自然语言生成的新算法将支持在不同的故事点提出不同类型的问题，并使用第一人称和直接语音生成故事事件，以允许角色说话。 类人儿童叙述者将模拟高即时性非语言行为，以增加参与和积极影响。该项目的原型将由加州大学戴维斯分校阅读和学术发展中心的儿童定期评估，因为该项目评估了这种探索性技术对提高社交语言和理解能力的承诺。

Practicing oral language skills supports children's later ability to process written text. Low socioeconomic status children receive lower levels of quality home language input, which negatively affects language development. Many children already suffer from a significant deficit in reading comprehension by the time they begin formal education, a deficit they may never overcome. This EArly Grant for Exploratory Research (EAGER) project will investigate improving children's home language environment with pedagogical learning agents, animated story characters and a child-like narrator who carry on a dialog with a child while telling a story. Story characters will talk directly to the child, tell the story from different perspectives, ask the child questions, phrase those questions in different ways using different vocabulary, and thus engage the child in understanding the story and improving their social language and listening comprehension skills. Pedagogical learning agents are a cheap and easily deployable technology that could readily be provided to children in any socioeconomic group.This project will develop technology for expressive interactive storytelling agents that model human conversational storytelling with a high level of social engagement. This will require methods to effectively manage a spoken dialog with a child, using speech recognition and text-to-speech output. The project will scaffold from a deep computational representation of narrative to generate different dialogs around the same story. Novel algorithms for natural language generation will support asking different types of questions at different story points and generation of story events with first person and direct speech to allow the characters to talk. The humanoid child narrator will model high immediacy nonverbal behaviors shown to increase engagement and positive affect. The project's prototype will be informed by regular evaluation by children at the UC Davis Reading and Academic Development Center, as the project assesses the promise of this exploratory technology to improve social language and comprehension skills.