Can Machines Learn Common-Sense Reasoning?

机器可以学习常识推理吗？

• 批准号: RGPIN-2022-05109
• 负责人: Emami, Ali
• 金额: $ 1.82万
• 依托单位: Brock University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749809
• 关键词: Machines; Learn; Common; Sense; Reasoning

Common-sense reasoning (CSR) has recently garnered a significant renewal of interest in the Artificial Intelligence community, resulting from the advent of certain technologies (e.g., dialogue systems, story understanding software, and recommendation tools) which can seem strikingly unintelligent in the absence of common sense. The goal of the proposed research is to make the learning of common-sense reasoning achievable for Machine Learning (ML) models, in order to help them generalize to complex scenarios that are encountered frequently in  the real world. Specifically, the research will first attempt to quantify the prevalence and classify the complexities of common-sense demanding problems in a variety of natural language understanding applications. The research will next proceed with studying strategies for the development of evaluation protocols that precisely identify how deep learning models make use of train instances to resolve problems at test time. When coupled with the first step of the research plan, these evaluation protocols will be able to provide us with a more nuanced understanding of the difficulty of instances for models. In addition, the research will explore incorporating in these protocols corrective measures for gender, racial and other forms of bias present in today's language generation models. Inspired by principles in software engineering, the use of all-purpose behavioural measures, such as invariance or minimum functionality tests, will also be explored and promoted as an important new benchmark measure for the current and future NLP systems. The next step of research will be dedicated to the development of novel deep learning models, based on the recently proposed Transformer architecture, which are potentially CSR-endowed, through modifying and augmenting various model components, including the attention mechanism, the decoder/encoder layers, and the training method. Some of the fundamental and technological questions that the research will address include: i) How can we develop a unified, generalized system that is capable of simultaneously tackling a number of difficult CSR tasks, as well as their more general, downstream counterparts with high coverage and interpretability; ii) How can we determine whether an improved performance on various Natural Language Processing (NLP) benchmarks represents a genuine progress towards common-sense-enabled systems; iii) How can we devise benchmarks that are more challenging, realistic and large-scale; iv) How can we use common-sense reasoning to help bridge the performance gap between robots and humans in the real world; and v) Can common-sense reasoning be used as a basis for developing end-to-end debiasing techniques that can combat various forms of biases prevalent in current pre-trained language models and corpora (gender/race/cultural)?

由于某些技术（如对话系统、故事理解软件和推荐工具）的出现，常识推理（common -sense reasoning， CSR）最近在人工智能社区中重新引起了人们的兴趣，这些技术在缺乏常识的情况下看起来非常不智能。提出的研究目标是使机器学习（ML）模型可以实现常识推理的学习，以帮助它们推广到现实世界中经常遇到的复杂场景。具体来说，该研究将首先尝试量化各种自然语言理解应用中常识要求问题的普遍性和复杂性。该研究下一步将继续研究评估协议的开发策略，以精确识别深度学习模型如何利用训练实例来解决测试时的问题。当与研究计划的第一步相结合时，这些评估协议将能够为我们提供对模型实例难度的更细致的理解。此外，本研究将探讨在这些协议中纳入针对当今语言生成模型中存在的性别、种族和其他形式偏见的纠正措施。受软件工程原理的启发，通用行为度量的使用，如不变性或最小功能测试，也将作为当前和未来NLP系统的重要新基准度量进行探索和推广。下一步的研究将致力于开发新的深度学习模型，基于最近提出的Transformer架构，通过修改和增强各种模型组件，包括注意机制，解码器/编码器层和训练方法，这可能是csr赋予的。本研究将解决的一些基本和技术问题包括：i)我们如何开发一个统一的、通用的系统，能够同时处理一些困难的企业社会责任任务，以及具有高覆盖率和可解释性的更一般的下游对应任务；ii)我们如何确定在各种自然语言处理（NLP）基准上的性能改进是否代表了向常识支持系统的真正进步；iii)我们如何设计更具挑战性、更现实和更大规模的基准；iv)我们如何使用常识推理来帮助弥合现实世界中机器人和人类之间的性能差距；v)常识性推理是否可以作为开发端到端去偏见技术的基础，以对抗当前预训练语言模型和语料库（性别/种族/文化）中普遍存在的各种形式的偏见？