Decoding speech comprehension from continuous EEG recordings

Human language is a remarkable manifestation of our cognitive abilities which is unique to our species. It is key to communication, but also to our faculty of generating complex thoughts. We organise, conceptualise, and share ideas through language. Neuroscience has shed insightful lights on our understanding of how language is processed by the brain although the exact neural organisation, structural or functional, underpinning this processing remains poorly known. This project aims to employ new methodology to understand speech comprehension during naturalistic listening condition. One achievement of this thesis lies in bringing evidence towards putative predictive processing mechanisms for language comprehension and confront those with rule-based grammar processing. Namely, we looked on the one hand at cortical responses to information-theoretic measures that are relevant for predictive coding in the context of language processing and on the other hand to the response to syntactic tree structures. We successfully recorded responses to linguistic features from continuous EEG recordings during naturalistic speech listening. The use of ecologically valid stimuli allowed us to embed neural response in the context in which they naturally occur when hearing speech. This fostered the development of new analysis tools adapted for such experimental designs. Finally, we demonstrate the ability to decode comprehension from the EEG signals of participants with above-chance accuracy. This could be used as a better indicator of the severity and specificity of language disorders, and also to assess if a patient in a vegetative state understands speech without the need for any behavioural response. Hence a primary outcome is our contribution to the neurobiology of language comprehension. Furthermore, our results pave the way to the development of a new range of diagnostic tools to measure speech comprehension of patients with language impairment.Open Acces

A Hybrid Machine Translation Framework for an Improved Translation Workflow

Over the past few decades, due to a continuing surge in the amount of content being translated and ever increasing pressure to deliver high quality and high throughput translation, translation industries are focusing their interest on adopting advanced technologies such as machine translation (MT), and automatic post-editing (APE) in their translation workflows. Despite the progress of the technology, the roles of humans and machines essentially remain intact as MT/APE are moving from the peripheries of the translation field closer towards collaborative human-machine based MT/APE in modern translation workflows. Professional translators increasingly become post-editors correcting raw MT/APE output instead of translating from scratch which in turn increases productivity in terms of translation speed. The last decade has seen substantial growth in research and development activities on improving MT; usually concentrating on selected aspects of workflows starting from training data pre-processing techniques to core MT processes to post-editing methods. To date, however, complete MT workflows are less investigated than the core MT processes. In the research presented in this thesis, we investigate avenues towards achieving improved MT workflows. We study how different MT paradigms can be utilized and integrated to best effect. We also investigate how different upstream and downstream component technologies can be hybridized to achieve overall improved MT. Finally we include an investigation into human-machine collaborative MT by taking humans in the loop. In many of (but not all) the experiments presented in this thesis we focus on data scenarios provided by low resource language settings.Aufgrund des stetig ansteigenden Übersetzungsvolumens in den letzten Jahrzehnten und gleichzeitig wachsendem Druck hohe Qualität innerhalb von kürzester Zeit liefern zu müssen sind Übersetzungsdienstleister darauf angewiesen, moderne Technologien wie Maschinelle Übersetzung (MT) und automatisches Post-Editing (APE) in den Übersetzungsworkflow einzubinden. Trotz erheblicher Fortschritte dieser Technologien haben sich die Rollen von Mensch und Maschine kaum verändert. MT/APE ist jedoch nunmehr nicht mehr nur eine Randerscheinung, sondern wird im modernen Übersetzungsworkflow zunehmend in Zusammenarbeit von Mensch und Maschine eingesetzt. Fachübersetzer werden immer mehr zu Post-Editoren und korrigieren den MT/APE-Output, statt wie bisher Übersetzungen komplett neu anzufertigen. So kann die Produktivität bezüglich der Übersetzungsgeschwindigkeit gesteigert werden. Im letzten Jahrzehnt hat sich in den Bereichen Forschung und Entwicklung zur Verbesserung von MT sehr viel getan: Einbindung des vollständigen Übersetzungsworkflows von der Vorbereitung der Trainingsdaten über den eigentlichen MT-Prozess bis hin zu Post-Editing-Methoden. Der vollständige Übersetzungsworkflow wird jedoch aus Datenperspektive weit weniger berücksichtigt als der eigentliche MT-Prozess. In dieser Dissertation werden Wege hin zum idealen oder zumindest verbesserten MT-Workflow untersucht. In den Experimenten wird dabei besondere Aufmertsamfit auf die speziellen Belange von sprachen mit geringen ressourcen gelegt. Es wird untersucht wie unterschiedliche MT-Paradigmen verwendet und optimal integriert werden können. Des Weiteren wird dargestellt wie unterschiedliche vor- und nachgelagerte Technologiekomponenten angepasst werden können, um insgesamt einen besseren MT-Output zu generieren. Abschließend wird gezeigt wie der Mensch in den MT-Workflow intergriert werden kann. Das Ziel dieser Arbeit ist es verschiedene Technologiekomponenten in den MT-Workflow zu integrieren um so einen verbesserten Gesamtworkflow zu schaffen. Hierfür werden hauptsächlich Hybridisierungsansätze verwendet. In dieser Arbeit werden außerdem Möglichkeiten untersucht, Menschen effektiv als Post-Editoren einzubinden

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Vector Semantics

This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways