Computational models of word meaning in use

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红眼睛这样的句子唤起的形象，我们需要做出一系列决定。从粗纹的眼睛(我们是在谈论棍棒还是飞行的哺乳动物？)到细纹的眼睛(说话者的意思是眼睛充血还是虹膜是红色的？)。人类处理意义显然很容易，这与目前计算系统的能力形成了鲜明对比。然而，如果我们想要改进Siri和Alexa等设备，以及更广泛地说，人工智能(AI)系统日益成为我们日常生活的一部分，让计算机更深入地理解含义是很重要的。在这个项目中，我将继续我的工作，整合不同科学学科的见解，构建以类似人类的方式执行语言相关任务的计算软件系统或模型。具体的项目目标围绕着这样一个问题：计算模型如何正确识别上下文中单词的预期解释，既包括粗粒度的(蝙蝠指的是棍棒还是哺乳动物)，也包括细粒度的(红眼中的红色是指什么)。我将探索新的计算公式，即单词如何对解释做出贡献，以及这些计算模型很少探索的信息来源，例如翻译数据。对于这些公式的开发，我从语言学和认知科学中对人类语言处理的研究中获得了启发--毕竟，没有人能像人类一样解释语言！*这个项目的一个重要方面是关注如何评估口译计算模型的有效性。目前，这种模型的评估依赖于例如具有相似性评级的不同上下文中的词对的数据集。这些数据集是有价值的起点，但也显示了局限性。首先，这些项目可能没有反映出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在哪些项目上测试模型来克服这一点。其次，为多种语言开发这样的数据集确保了计算解释模型对英语以外的语言的普适性。第三，相似性评分(和可比较的任务)是有意识的衡量标准，可能会引入各种不想要的偏见。利用潜意识过程中的语言处理心理学实验的数据来测试这些模型，是评估我打算在这个项目中开发的模型的另一种方式。*结合在一起，以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。

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.**