论文标题
不要在图灵测试中作弊：迈向人工智能中的基础语言学习
Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence
论文作者
论文摘要
最近围绕语言处理模型的复杂性的最新炒作使人们对机器获得了类似人类的自然语言指挥的乐观情绪。在人工智能中，自然语言理解领域（NLU）的研究声称，在这一领域取得了长足的进步，但是，缺乏概念上的清晰度/一致性，即如何在该领域和其他学科中使用“理解”，这使得我们很难辨别我们的实际程度。在这篇跨学科研究论文中，我整合了认知科学/心理学，心理哲学和认知语言学的见解，并对它进行了对NLU当前方法的批判性审查，以探索基本要求的批判性审查 - 持续挑战 - 持续发展人工智能的系统，并具有类似人类的语言使用和理解的能力。
Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.
