论文标题
类似人类的通用语言处理
Human-like general language processing
论文作者
论文摘要
使用语言使人类在智慧中超越动物。为了让机器灵活地理解，学习和使用语言，我们提出了类似人类的通用语言处理（HGLP）体系结构，其中包含感觉运动，关联和认知系统。 HGLP网络像孩子一样从轻松到硬学习，通过共同激活多模式神经元来理解单词含义，通过实时构建虚拟世界模型来理解和生成句子，并可以口头表达整个思维过程。 HGLP迅速学习了10多个不同的任务，包括对象识别，句子理解，注意力控制，查询，推理，运动判断，混合算术操作，数字跟踪和写作以及以语言为指导的人类迭代思维过程。 HGLP框架中的语言不匹配，相关统计，而是可以描述和控制想象力的脚本。
Using language makes human beings surpass animals in wisdom. To let machines understand, learn, and use language flexibly, we propose a human-like general language processing (HGLP) architecture, which contains sensorimotor, association, and cognitive systems. The HGLP network learns from easy to hard like a child, understands word meaning by coactivating multimodal neurons, comprehends and generates sentences by real-time constructing a virtual world model, and can express the whole thinking process verbally. HGLP rapidly learned 10+ different tasks including object recognition, sentence comprehension, imagination, attention control, query, inference, motion judgement, mixed arithmetic operation, digit tracing and writing, and human-like iterative thinking process guided by language. Language in the HGLP framework is not matching nor correlation statistics, but a script that can describe and control the imagination.
