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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739185
• 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“蝙蝠有红眼睛”这样的句子唤起的图像，我们需要做出一系列决定，这些决定包括粗粒度的决定（我们是不是）。谈论俱乐部或飞行的哺乳动物？），到细粒度的（说话者的意思是眼睛充血还是虹膜是红色的？）人类处理意义的明显轻松与当前形成鲜明对比。尽管如此，计算系统的能力。如果我们想要改进 Siri 和 Alexa 等设备，更广泛地说，人工智能 (AI) 系统日益成为我们日常生活的一部分，那么让计算机对意义进行更深入的理解就很重要。在这个项目中，我将继续我的研究。整合不同科学学科的见解来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，具体项目目标围绕计算模型如何正确识别预期解释的问题。上下文中的单词，都在粗粒度（无论蝙蝠是指俱乐部还是哺乳动物）和细粒度（红眼睛中的红色意味着什么）级别我将探索单词如何有助于解释的新颖计算公式以及很少探索的来源。这些计算模型的信息，例如翻译数据，为了开发这些公式，我从语言学和认知科学中的人类语言处理研究中汲取灵感——毕竟，没有人能够像人类一样解释语言！一个重要方面该项目的重点是评估解释计算模型的有效性的方法，例如，这些模型的评估依赖于不同上下文中具有相似性评级的单词对的数据集，但这些数据集是有价值的起点。首先，这些项目可能无法反映人工智能系统在“野外”面临的解释任务的范围，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问题。多种语言确保计算解释模型对语言的泛化性第三，相似性评级（和类似任务）是有意识的测量，这可能会引入各种不必要的偏差，利用无意识过程的语言处理实验数据来测试模型是评估我想要的模型的另一种方式。在这个项目中共同开发以类人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，允许更多开放式计算工具以符合人类期望的方式解释语言。对话。