1 Position (TV-L 13 65%) available
We invite applications for a position in a DFG-founded project on “The Role of Language Experience and Visual Context in Surprisal” that is part of a CRC Information Density and Linguistic Encoding (SFB 1102). Work towards a PhD degree, as part of this position, is possible and encouraged. This project will examine the interplay of linguistic and visual context in determining surprisal, and their interdependence with language development and individual differences. The goal is to go beyond exploring the probabilistic (language) system that adults have established over time and consider the development of such a system in childhood: Does limited experience lead to simplified predictions, which would likely lead to more frequent (prediction) errors eliciting high surprisal? The project will also examine the effect of prediction, and prediction error, on acquiring and storing novel word meanings in both children and adults. Building on the findings from the first phase, the relation between a word’s expectancy and its induced cognitive load, as well as the role of visual context and the individual linguistic and cognitive abilities, will be considered. Exploring this across development will contribute to our understanding of how and when these factors interact with each other and potentially provide insights into possible connections between prediction, error/surprisal and learning. Candidates for this position should have a master’s degree in psychology, psycholinguistics or a related discipline. Experience with psychophysiological methods (eye-tracking, in particular) as well as knowledge about statistical analysis techniques is expected. Since experiments will be conducted in German and with children, a good working knowledge of German is necessary.
Please check the offical job advertisement at Saarland University’s website for more information on the application process.
Project A1: Neurobehavioural Correlates of Surprisal in Online Comprehension
PIs: Matthew Crocker and Harm Brouwer
This project continues to investigate how world knowledge about likely events and probabilistic linguistic experience combine to determine a person’s expectations, and thus “semantic surprisal”, during online sentence comprehension (Venhuizen, Crocker & Brouwer, 2018). In phase two, electrophysiological and behavioural experiments will be used to test the predictions, and inform the further development, of a neurocomputational model of sentence comprehension (Brouwer, Crocker, Venhuizen, & Hoeks, 2017) with the aim of explicitly linking semantic surprisal to the underlying neurocognitive processes that are indexed by event-related brain potentials (ERPs). We will carry out a set of neurophysiological and reading time experiments to test three predictions that follow from the model: 1) that the P600 is an index of interpretation-level surprisal, 2) that surprisal reflects the generation of forward and backward inferences, and 3) that surprisal reflects the interaction of world knowledge and linguistic experience. By arriving at a neurocomputational model that links electrophysiological and behavioural metrics of processing difficulty, this project thus seeks to illuminate the neurocognitive basis of surprisal.
PhD Student (TV-L 13 65%) – Experimental Psycholinguist: Candidates for this position should have completed a masters degree in psycholinguistics, or a related discipline. Experience with psycholinguistic methods, such as ERPs and/or eye-tracking, as well as associated inferential statistical analysis techniques is expected. As experiments are conducted primarily in German, a good working knowledge of the language is also desired.
Project A4: Language Comprehension in a Noisy Channel
PIs: Vera Demberg, Jutta Kray, Dietrich Klakow
In realistic environments, language comprehension depends not only on the amount of information that needs to be transferred, but also on the quality of this information transfer. Existing research shows that a substantial portion of the comprehension difficulty in elderly adults might be due to perceptual problems with hearing or vision (when reading) and/or to cognitive problems. In the experimental part of the project, we plan to vary the quality of information transfer by degrading the auditory speech signal or by inducing environmental noise. We will compare groups of younger and older adults. In the modelling part, we propose a noisy channel model, consisting of a component that models comprehension at different levels of hearing ability, and a generation component that can model the confusability of words and can in turn optimize the system-generated output in order to minimize confusability of words, while also adapting the output to a target channel capacity.
PhD Student (TV-L 13 65%) – Candidates for this position should have completed a masters degree in psychology or psycholinguistics, or a related discipline. Experience with psychophysiological methods, such as ERPs, as well as knowledge about statistical analysis techniques is expected. The experiments will be conducted in German and younger as well as older adults will be tested. Therefore, some knowledge of the German language as well as background knowledge in cognitive and developmental psychology is desired.
PhD Student (TV-L 13 65%) – Candidates for this position should have completed a MSc degree in phonetics, psycholinguistics or computational linguistics, or a related discipline. Experience with eye-tracking as well as knowledge about statistical analysis is expected. Background knowledge about phonetics and experience in working with spoken language is a plus. Experiments will be conducted in German. A focus of this PhD project will be on the phenomenon of “false hearing” in various noise conditions, involving younger as well as older adults. False hearing refers to cases where the hearer is confident to have heard something other than what the speaker said. Furthermore, this PhD student will investigate the role of the uniform information density in understanding infrequent words in noisy conditions and with limited attention (during dual tasking).
PhD Student (TV-L 13 75%) – Candidates for this position should have completed a MSc degree in computer science or computational linguistics, or a related discipline. A strong background in machine learning (including neural networks) is expected. This PhD project will focus on quantifying the phonetic similarity between words and utterances, and creating a natural language generation system which avoids generating utterances that are auditorily difficult to understand or can be misunderstood easily for something else.
Project B6: Neural Feature and Representation Learning for Information Density Based Translationese Classification
PIs: Josef van Genabith, Raphael Rubino
In this project we are using deep learning and information theory to model key aspects of human and machine translation. The objective is to better understand human translation to improve machine translation.
PhD Student (TV-L 13 75%) – in Deep Learning for Modelling Machine Translation and Human Translation. Requirements: MSc/MA (or BSc/BA) in Natural Language Processing, AI or Computer Science. Strong programming, mathematics, problem solving, creativity, analytic capabilities and independent thinking. Strong interest and expertise in language, natural language processing, statistics, machine learning, and deep learning. Able to work well in a team. Good writing and communication skills in English. German language skills are a plus, but not required.
Postdoctoral Researcher (TV-L 13 50%) – in Deep Learning for Modelling Machine Translation and Human Translation. Requirements: PhD in Machine Translation, Natural Language Processing, Machine Learning, AI or Computer Science. Strong problem solving, creativity, analytic capabilities, independent thinking, mathematics, statistics, machine learning, deep learning and software development. Strong track record in international peer reviewed publications. Able to work well in a team. Good writing and communication skills in English. German language skills are a plus, but not required.