Is technology really as innocent and as objective as they are said to be? As machine learning (ML) and Artificial Intelligence (AI) becomes more prominent in our life from speech and voice recognition by Alexa to automatic fake news warnings of social media posts, issues with social bias and fairness in language technology become more pertinent than ever before. Negative impacts that biased ML and AI could have for various social identities such as race, gender and culture. We first introduce the concept of bias in language technology, and the different types of biases such as racial, gender, cultural biases. To begin to understand the cause of these biases, we will cover the basic underlying structure of some of the technologies such as Automatic Speech Recognition, hate speech detection and word association. To evaluate these biases, we will learn to generate test cases that can be used to evaluate trained systems, and the metrics that are used for measuring bias/fairness. Finally, we will cover the basics of bias mediation and techniques.
This colloquium is for all students who want to discuss their project for a Bachelor, Master or doctoral thesis and who wish to receive feedback and support. The colloqium takes place every second week in person. The other weeks you would be required to work as a group. We will use the first session to decide on the topics of presentation, which will then have to become a part of the colloquium`s program. In the in-person weeks, we will also cover research related skills, such as time-management, hypothesis generation, critical reading and more.
Have you ever wondered why we (humans) are really good at changing words? Like, if there`s more than one cat, we just add an -s to make it "cats". But, when we see more than one child, we make it "children". How do we know which pattern to follow? In this course, we will explore how humans learn these word changes. Specifically, we will look into how humans express a wide range of meanings and relationships without inventing new words (i.e we are looking at inflection). We will start by looking at prominent theories that help us understand how both children and adults acquire this knowledge of inflection. We will also engage broadly with the computational modeling approaches that help us better understand the complex process of language acquisition. As part of the course requirements, you are not required to do any programming.The course involves reading and understanding current research papers in the field of child and adult language acquisition, computational morphology, and phonology (mainly, SIGMORPHON (https://sigmorphon.github.io/) papers).
It is as necessary to be numerate as it is to be literate, but students in the field of humanities are often not as numerate as they are literate. They will need to evaluate evidence that are based on probability-based models or statistical results in many of the courses that they take in university, as they consider the efficacy of vaccination and the severity of the pandemic, as they begin to vote in local and national elections, as they search for employment on the job market after graduating, and so on. With an increasingly digital world filled with big data, a command of statistical reasoning is more important than ever. In this course, we will learn numeracy through linguistics, specifically through phonetics and phonology by learning to analyse the sounds of languages quantitatively. How do we analyse the sounds of languages quantitatively? This course, Analysing the sounds of languages, covers the basics of quantitative methods using real data taken from the field of phonetics and phonology. We will provide a gentle introduction to the statistical program R (www.r-project.org) -- a program that is used by data scientists in the tech. industry and academic researchers. The course will consist of a combination of lectures, and plenty of hands-on exercises. We introduce research questions, such as “Do Southerners in the US really talk more slowly?” or “Why do we expect scholarly words to be longer than familiar words?” as a framework for introducing the numerical concepts required to answer research questions such as these. In this course, statistical methods are introduced with a research question and a solid understanding of the data, which is why we use real data and questions that are relevant to anyone who commands a spoken language. A good amount of space is also devoted to illustrating how to formulate and answer a research question, and hypothesis development and testing.
Have you ever been curious about why Voice Assistants (Siri, Amazon Echo and Google Assistant) sometimes struggle to understand your English accent? As linguists, we can play a crucial role in evaluating these sophisticated systems. In this class, you will assess the performance of Automatic Speech Recognition systems (ASR) by evaluating them against your own recorded speech and other existing speech recordings. You will learn how to use recording equipment in a laboratory setting as well as how to cut and prepare audio for further processing.
Natural Language Processing plays a big role in our digital lives. We will demystify some of these everyday tasks that involve natural language processing: such as spelling and grammar correction, document classification, dialogue systems, machine translation, and forensic linguistics. On the practical side, we will focus on applying off-the-shelf tools that are often used in computational modelling of language data. Armed with these skills, you will be able to model language data quantitatively and ask measurable research questions.
In this course we will be discussing the basic notions, terminology and methodology of modern linguistics. We will focus on core areas of English linguistics.In this first part of the Introduction to English language and linguistics we will deal with the structure of language (in particular of the English language) and acquire the terminology and methodology of modern linguistics. The focus will be on the core areas of linguistics: the study of sound structure (phonology), word structure (morphology), and sentence structure (syntax).In the second part of the Introduction to English language and linguistics, the formal levels of linguistic analysis (phonology, morphology, syntax) will be viewed in relation to their meaning(s) as well as to their contexts of use. Topics therefore include semantics, pragmatics, and sociolinguistics.
Language and Health is a seminar course that explores the intersection between language and heatlh. Through research papers, we will gain a better understanding of how language plays a pivotal role in both assessing health conditions and effectively conveying vital health information. Throughout the course, students will delve into the various dimensions of language (spoken speech, written text, sociolinguistic features of non-standard varieties) and its implications for individual and public health.
This course provides you with an elementary introduction to English phonetics and phonology, designed for those who have no previous knowledge whatsoever of the subject. It begins with a very elementary introduction to articulatory phonetics, and then proceeds to introduce the student to a very simplified account of some of the main aspects of the phonological structure of present-day English. Languages other than English will also be examined to compare and contrast the linguistic structural differences and gain insights on linguistic generalisation.
Recent advances in both computing power and statistical thinking have allowed the use of huge amounts of data in a way not feasible in earlier times. In this course, we will learn how to use these new methods and data sources to investigate the structure of language. Of course, language is a multimodal phenomenon, i.e., it exists not just in the form of written text, but also in spoken form, and these different modes require different approaches. We will learn about the different methods needed to approach these different modalities of language. In learning about these statistical applications, we will be making use of the commonly used R programming language. Previous knowledge of R would of course make things easier, but is not a prerequisite for participation. Potential students who are very apprehensive about programming may also want to consider the parallel course (with the same title of Corpus Linguistics), taught by my colleague Ghattas Eid, which will have less of a computational focus.