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像蝙蝠这样的句子的句子唤起的形象，我们需要做出许多决定。这些范围从粗粒粒度（我们是在谈论俱乐部还是飞行的哺乳动物？），到细粒度的（扬声器意味着眼睛是流血的，还是虹膜是红色的？）。与当前计算系统的当前能力形成鲜明对比的是，人类过程的明显易于性。然而，如果我们想改善像Siri和Alexa这样的电器，以及更普遍的人工智能（AI）系统，使计算机能够更深入地了解含义很重要，这些设备越来越多地构成我们日常生活的一部分。 在这个项目中，我将继续我的工作范围，集成了不同科学学科的见解，以构建以人类方式执行与语言相关的任务的“模型”。具体的项目目标围绕以下问题，一个计算模型如何在上下文中正确识别对单词的预期解释，无论是在粗糙的粒度（无论是指俱乐部还是哺乳动物）和更细粒度（bat）红色眼睛红色）水平。我将探讨单词如何有助于这些计算模型（例如翻译数据）的新颖计算公式以及很少探索的信息来源。为了开发这些表述，我从语言学和认知科学中人类语言处理的研究中汲取灵感 - 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关注评估解释计算模型的有效性的方法。当前，对此类模型的评估取决于在不同上下文中具有相似性评分的不同语境中成对的单词的数据集。这些数据集是宝贵的起点，但也显示出限制。首先，这些项目可能无法反映AI系统“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哪些项目来克服。其次，为多种语言开发此类数据集可确保计算解释模型对英语以外的语言的普遍性。第三，相似性等级（和可比任务）是有意识的措施，这可能引入各种不需要的偏见。从心理实验的数据上测试模型对语言处理的数据，这是评估我打算在该项目中开发的模型的另一种方法。 这部小说共同采用了以人类的方式来解释语言的方法，以及评估这些方法的更强大的方法，允许以与人类期望在对话中的期望相匹配的方式来解释语言的更多开放式计算工具。