基于儿童语言习得机制的语言接地技术研究

项目名称： 基于儿童语言习得机制的语言接地技术研究

项目编号： No.61273365

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王小捷

作者单位： 北京邮电大学

项目金额： 80万元

中文摘要： 语言接地连接语言与感知，是认知科学中的一个重要问题，这一问题的解决对于研制智能服务机器人具有重要价值。儿童语言习得依赖于内部大脑结构以及与外部环境的交流，这两个因素协同作用，是儿童分阶段逐步习得接地语言的关键。本项目拟建模这种儿童语言习得机制，在语言接地技术上取得突破。首先提出一种能对视、听双模态信息分别进行深层加工，并在深层表示间建立关联的混合深层网络模型，构建其学习和评测方法。同时，建模儿童语言习得的阶段性机制，使模型在统一的深层网络结构框架下适应不同阶段的语言特点；其次对基于视、听双模态信息的监护人与儿童对话进行建模，提出监护人说话意图的分析方法；进而研究由说话人意图驱动的深层网络学习和测试控制机制，建模儿童语言习得时外部交流与内部结构间的协同作用。最后将这些技术集成部署在一个机器人上，实现一个能基于视、听双通道信息与人进行自然交互，具有分阶段逐步习得单词和双词组合接地语言的机器人。

中文关键词： 对应自编码器；关联学习；联合模型；层次长短期记忆模型；部分可观马尔科夫决策过程

英文摘要： Human language can describe external perpceptive world. The association between langauge and perveptive information is called language grounding. Language Grounding is a kernel problem of cognitive science. The resolution of this problem is also very important for the development of intelligence servive robots. It is now well acknowledged that children langauge acquisition mainly dependend on the highly complex brain and communications with external envirnoment, especially dialogues with their babycarers. These two factors and their tight co-operation compose the kernel mechanism of children language acquisition. This project plan to model the mechanism of children language acquisition, and make progresses in langauge grounding. We firstly propose a hybrid deep network model. The model includes two deep networks and one counter-propagation network. Two deep networks are learnt to analysis the visul and audio stream seperately, internal representations are formed in deep network after learning. Counter-propagation network is then used to build links between two internal representations. We also model different stages of children language acquisition in the same network frame. Two models with same network frame are proposed to model the internal mechanism of grounded language acquisition in both stages of one-word

英文关键词： Correspondence Auto-Encoder；Correlation Learning；Joint Model；Hierarchical Long Short-Term Memory；Partial Observable Markov Decision Process