Identifying and Modeling Code-Switched Language

Code-switching is the phenomenon by which bilingual speakers switch between multiple languages during written or spoken communication. The importance of developing language technologies that are able to process code-switched language is immense, given the large populations that routinely code-switch. Current NLP and Speech models break down when used on code-switched data, interrupting the language processing pipeline in back-end systems and forcing users to communicate in ways which for them are unnatural. There are four main challenges that arise in building code-switched models: lack of code-switched data on which to train generative language models; lack of multilingual language annotations on code-switched examples which are needed to train supervised models; little understanding of how to leverage monolingual and parallel resources to build better code-switched models; and finally, how to use these models to learn why and when code-switching happens across language pairs. In this thesis, I look into different aspects of these four challenges. The first part of this thesis focuses on how to obtain reliable corpora of code-switched language. We collected a large corpus of code-switched language from social media using a combination of sets of anchor words that exist in one language and sentence-level language taggers. The newly obtained corpus is superior to other corpora collected via different strategies when it comes to the amount and type of bilingualism in it. It also helps train better language tagging models. We also have proposed a new annotation scheme to obtain part-of-speech tags for code-switched English-Spanish language. The annotation scheme is composed of three different subtasks including automatic labeling, word-specific questions labeling and question-tree word labeling. The part-of-speech labels obtained for the Miami Bangor corpus of English-Spanish conversational speech show very high agreement and accuracy. The second section of this thesis focuses on the tasks of part-of-speech tagging and language modeling. For the first task, we proposed a state-of-the-art approach to part-of-speech tagging of code-switched English-Spanish data based on recurrent neural networks.Our models were tested on the Miami Bangor corpus on the task of POS tagging alone, for which we achieved 96.34% accuracy, and joint part-of-speech and language ID tagging,which achieved similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). For the task of language modeling, we first conducted an exhaustive analysis of the relationship between cognate words and code-switching. We then proposed a set of cognate-based features that helped improve language modeling performance by 12% relative points. Furthermore, we showed that these features can also be used across language pairs and still obtain performance improvements. Finally, we tackled the question of how to use monolingual resources for code-switching models by pre-training state-of-the-art cross-lingual language models on large monolingual corpora and fine-tuning them on the tasks of language modeling and word-level language tagging on code-switched data. We obtained state-of-the-art results on both tasks

Library Publishing Curriculum Textbook

In the original, modular curriculum (2018) on which this textbook is based, each unit of the Library Publishing Curriculum contained an instructor’s guide, narrative, a slideshow with talking notes, bibliographies, supplemental material, and activities for use in a physical or virtual classroom for workshops and courses. This textbook version, produced in 2021, adapts the original narrative as the primary content (with very little additional editing) and incorporates the bibliographies, appendices, and images from the slideshow into a linear reading and learning experience for use by librarians or students learning on their own or as part of a classroom learning experience. The LPC hopes others use and extend this CC-BY version into even more learning opportunities to help create a more equitable publishing ecosystem

Finding out you have Type 1 Diabetes: understanding what is known about the development of mental health difficulties in children and young people with Type 1 Diabetes and how they experience being diagnosed

In 2019, Diabetes UK released a statement requesting more research into the mental health needs of people living with Type 1 Diabetes (T1D). The following thesis aims to add to the evidence base by exploring what may leave Children and Young People (CYP) vulnerable to developing mental health difficulties and how they experience their diagnosis and ensuing period of living with T1D. Part One describes a scoping review, mapping out literature on the development of mental health difficulties in CYP with T1D. Of the publications retrieved, 85 are included in the analysis. Possible factors that leave CYP vulnerable to the development of mental health difficulties are collated into: T1D-related, demographic, personal, systemic, health-related factors and the presence of other mental health difficulties. Findings highlight the need for further systematic reviews and exploration into the impact of the T1D diagnosis on CYP. Part Two describes an Interpretative Phenomenological Analysis (IPA) study into CYPs’ experiences of being diagnosed with T1D and ensuing period of living with T1D, including the healthcare received. Ten adolescents participated. Identified themes are: life becoming uncertain, T1D becoming a reality, adapting to the permanence of T1D, seeing the self as different and the role of others in making life with T1D tolerable. Participants highlighted the emotional impact of their diagnosis and factors that aid or hinder the process of adaption. Part Three speaks to the primary researcher’s reflections on issues surrounding the research process. Personal experiences and assumptions, dilemmas and possible avenues for additional research are discussed

Artificial intelligence as a tool for research and development in European patent law

Artificial intelligence (“AI”) is increasingly fundamental for research and development (“R&D”). Thanks to its powerful analytical and generative capabilities, AI is arguably changing how we invent. According to several scholars, this finding calls into question the core principles of European patent law—the field of law devoted to protecting inventions. In particular, the AI revolution might have an impact on the notions of “invention”, “inventor”, “inventive step”, and “skilled person”. The present dissertation examines how AI might affect each of those fundamental concepts. It concludes that European patent law is a flexible legal system capable of adapting to technological change, including the advent of AI. First, this work finds that “invention” is a purely objective notion. Inventions consist of technical subject-matter. Whether artificial intelligence had a role in developing the invention is therefore irrelevant as such. Nevertheless, de lege lata, the inventor is necessarily a natural person. There is no room for attributing inventorship to an AI system. In turn, the notion of “inventor” comprises whoever makes an intellectual contribution to the inventive concept. And patent law has always embraced “serendipitous” inventions—those that one stumbles upon by accident. Therefore, at a minimum, the natural person who recognizes an invention developed through AI would qualify as its inventor. Instead, lacking a human inventor, the right to the patent would not arise at all. Besides, the consensus among scholars is that, de facto, AI cannot invent “autonomously” at the current state of technology. The likelihood of an “invention without an inventor” is thus remote. AI is rather a tool for R&D, albeit a potentially sophisticated one. Coming to the “skilled person”, they are the average expert in the field that can rely on the standard tools for routine research and experimentation. Hence, this work finds that if and when AI becomes a “standard” research tool, it should be framed as part of the skilled person. Since AI is an umbrella term for a myriad of different technologies, the assessment of what is truly “standard” for the skilled person – and what would be considered inventive against that figure – demands a precise case-by-case analysis, which takes into account the different AI techniques that exist, the degree of human involvement and skill for using them, and the crucial relevance of data for many AI tools. However, while AI might cause increased complexities and require adaptations – especially to the inventive step assessment – the fundamental principles of European patent law stand the test of time

Bowdoin Orient v.137, no.1-25 (2007-2008)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1008/thumbnail.jp