Computational models of word meaning in use

使用中词义的计算模型

• 批准号: RGPIN-2019-06917
• 负责人: Beekhuizen, Barend
• 金额: $ 1.68万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716308
• 关键词: Computational; models; word; meaning; use

The human capacity for understanding meaning is impressive: in order to understand the image a speaker is trying to evoke with a sentence like The bat has red eyes, we need to make a host of decisions. These range from coarse-grained ones (are we talking about the club or the flying mammal?), to fine-grained ones (does the speaker mean that the eyes are bloodshot or that the irises are red?). The apparent ease with which humans process meaning stands in stark contrast with the current capacities of computational systems. Nevertheless, allowing computers to arrive at a deeper understanding of meaning is important if we want to improve appliances like Siri and Alexa, and more generally, the Artificial Intelligence (AI) systems that increasingly form part of our everyday life. In this project, I will continue my line of work integrating insights from different scientific disciplines to build computational software systems or 'models' that carry out language-related tasks in human-like ways. The specific project goals are centred around the question how a computational model can correctly identify the intended interpretation of a word in context, both at the coarser-grained (whether bat refers to the club or the mammal) and finer-grained (what is meant with red in red eyes) level. I will explore novel computational formulations of how words contribute to the interpretation as well as little-explored sources of information for these computational models, such as translation data. For the development of these formulations, I draw inspiration from the study of human language processing in linguistics and cognitive science -- after all, no one can interpret language as well as humans can! An important aspect of this project is a focus on ways of assessing the validity of computational models of interpretation. Currently, the evaluation of such models relies on datasets of, for instance, pairs of words in different contexts with similarity ratings. These datasets are valuable starting points, but also display limitations. First, the items may not reflect the range of interpretation tasks an AI system faces 'in the wild', which I intend to overcome by looking more closely into what items we test the models on. Second, developing such datasets for multiple languages ensures the generalizability of the computational interpretation models to languages beyond English. Third, similarity ratings (and comparable tasks) are conscious measures, which may introduce various kinds of unwanted biases. Testing the models on data from psychological experiments on language processing that tap into unconscious processes is another way of evaluating the models I intend to develop in this project. Together, the novel approaches that interpret language in a human-like way, as well as stronger ways of evaluating those approaches, allow for more open-ended computational tools that interpret language in a way that matches human expectations in conversation.

人类理解意义的能力令人印象深刻：为了理解说话者试图用一句话（如蝙蝠有红眼睛）唤起的形象，我们需要做出大量决定。这些范围从粗粒的（我们谈论的是俱乐部或飞行哺乳动物？），到细粒度的（说话者的意思是眼睛充血还是虹膜是红色的？）人类处理意义的表面上的轻松与当前计算系统的能力形成鲜明对比。然而，如果我们想改进Siri和Alexa等设备，以及更普遍的人工智能（AI）系统，让计算机更深入地理解意义是很重要的，这些系统越来越成为我们日常生活的一部分。 在这个项目中，我将继续我的工作，整合来自不同科学学科的见解，以构建计算软件系统或“模型”，以类似人类的方式执行与语言相关的任务。具体的项目目标围绕着这样一个问题：计算模型如何在上下文中正确识别单词的预期解释，无论是在粗粒度（蝙蝠是指俱乐部还是哺乳动物）还是细粒度（红眼睛中的红色是什么意思）水平。我将探索新的计算公式，说明单词如何有助于解释，以及这些计算模型的信息来源，如翻译数据。对于这些公式的发展，我从语言学和认知科学中对人类语言处理的研究中获得灵感-毕竟，没有人能像人类一样解释语言！ 该项目的一个重要方面是侧重于评估解释的计算模型的有效性的方法。目前，这种模型的评估依赖于数据集，例如，在不同的上下文中具有相似性评级的单词对。这些数据集是有价值的起点，但也显示出局限性。首先，这些项目可能无法反映人工智能系统在“野外”面临的口译任务的范围，我打算通过更仔细地研究我们测试模型的项目来克服这一点。其次，为多种语言开发这样的数据集可以确保计算口译模型对英语以外的语言的通用性。第三，相似性评级（和可比较的任务）是有意识的措施，这可能会引入各种不必要的偏见。在语言处理的心理学实验数据上测试模型，这些实验利用了无意识过程，这是评估我打算在这个项目中开发的模型的另一种方式。 总之，以类似人类的方式解释语言的新方法，以及评估这些方法的更强大的方法，允许更多的开放式计算工具，以符合人类在对话中的期望的方式解释语言。