TU Kaiserslautern | DFKI
Shailza Jolly is a second-year Ph.D. student, advised by Prof. Dr. Andreas Dengel at TU Kaiserslautern in Germany and works as a research assistant at the German Research Center for Artificial Intelligence (DFKI). She is primarily interested in developing machine learning methods for building low-resource natural language generation and understanding systems. Presently, she is working on scoring-based NLG methods in collaboration with Prof. Mou from the University of Alberta, Canada. Her other research interests include vision and language systems, interpretability, and conversational AI. She completed her master's in computer science from TU Kaiserslautern and spent a semester abroad at Kyushu University in Japan, where her work "How do Convolution Neural Networks Learn Design?" won the best student paper award at ICPR 2018. She has published her works at venues like EMNLP and COLING. During her graduate studies, she interned at SAP Machine Learning Research (Berlin, Germany), and Amazon Alexa (Aachen, Germany). Recently, she has been awarded an STSM Grant under Multi3Generation COST action to conduct research for generating fact-checking explanations in low-resource settings in collaboration with Prof. Augenstein at the University of Copenhagen, Denmark.
Is it possible to have human-like conversations with chatbots by training them using a handful of samples? Can small businesses and startups build robust and interpretable NLP systems without extensive computing infrastructure and large datasets?