The HiTZ center hosts a webinar series on Language Technology with talks by key researchers in the field. Please find below a list of upcoming webinars, as well as recordings of past webinars.

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future webinars

Maarit Koponen (University of Eastern Finland)

Machine translation as a tool for multilingual information: different users and use scenarios

(December 1, 2022, 15:00 CET)

Summary: Recent advances in machine translation quality have improved its usefulness as a tool to satisfy the demand for multilingual information and communication. Machine translation is nowadays a common part of professional translation workflows, but it is not a tool exclusive to translators. Users of machine translation can be found, for example, in public service institutions and newsrooms looking to produce and disseminate information in multiple languages. At the same time, machine translation can also offer a way for people to access information that may not otherwise be available in their language. Effective and responsible use of machine translation, however, requires a clear understanding of the potential risks as well as potential benefits. In this talk, I discuss how machine translation is used for producing and accessing information and how various situational factors affect its use in different scenarios.
Bio: Dr Maarit Koponen currently works as Professor of Translation Studies at the University of Eastern Finland. She has previously worked as a post-doctoral researcher at the University of Helsinki and as a lecturer at the University of Turku after receiving her PhD in Language Technology at the University of Helsinki in 2016. Her research focuses on translation technology, particularly machine translation, and the effect of technology on translation both in professional and non-professional settings. Starting in October 2022, Koponen leads a work package focusing on linguistic barriers to information accessibility and technological solutions as part of the research project DECA (Democratic epistemic capacities in the age of algorithms), funded by the Academy of Finland Strategic Research Council. She chairs Working Group 7 “Language work, language professionals” of the EU COST Action “Language in the Human-Machine Era” (LITHME). She has also worked as a professional translator for several years.

past webinars

You can watch past seminars here

Vered Shwartz (The University of British Columbia-Vancouver)

Incorporating Commonsense Reasoning into NLP Models (November 3, 2022, 15:30 CET)   

Summary: NLP models are primarily supervised, and are by design trained on a sample of the situations they may encounter in practice. The ability of models to generalize to and address unknown situations reasonably is limited, but may be improved by endowing models with commonsense knowledge and reasoning skills. In this talk, I will present several lines of work in which commonsense is used for improving the performance of NLP tasks: for completing missing knowledge in underspecified language, interpreting figurative language, and resolving context-sensitive event coreference. Finally, I will discuss open problems and future directions in building NLP models with commonsense reasoning abilities.
Bio: Vered Shwartz is an Assistant Professor of Computer Science at the University of British Columbia and a faculty member at the Vector Institute for Artificial Intelligence. Her research interests include commonsense reasoning, computational semantics and pragmatics, and multiword expressions. Previously, Vered was a postdoctoral researcher at the Allen Institute for AI (AI2) and the University of Washington, and received her PhD in Computer Science from Bar-Ilan University.

Xiang Ren (University of Southern California - USC)

Commonsense Reasoning in the Wild (October 6, 2022, 17:00 CET)   

Summary: Current NLP systems impress us by achieving close-to-human performance on benchmarks of answering commonsense questions or writing interesting stories. However, most of the progress is evaluated using static, closed-ended datasets created for individual tasks. To deploy commonsense reasoning services in the wild, we look to develop and evaluate systems that can generate answers in an open-ended way, perform robust logical reasoning, and generalize across diverse task formats, domains, and datasets. In this talk I will share our effort on introducing new formulations of commonsense reasoning challenges and novel evaluation protocols, towards broadening the scope in approaching machine common sense. We hope that such a shift of evaluation paradigm would encourage more research on externalizing the model reasoning process and improving model robustness and cross-task generalization.
Bio: Xiang Ren is an assistant professor and Viterbi Early Career Chair at the USC Computer Science Department, a Research Team Leader at USC ISI, and the director of the Intelligence and Knowledge Discovery (INK) Lab at USC. Priorly, he spent time as a research scholar at Stanford University and received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. Ren's research seeks to build generalizable natural language processing (NLP) systems which can handle a wide variety of language tasks and situations. He works on new algorithms and datasets to make NLP systems cheaper to develop and maintain, arm machine models with common sense, and improve models’ transparency and reliability to build user trust. His research work has received several best paper awards in top NLP and AI conference venues. Ren has been awarded a NSF CAREER Award, multiple faculty research awards from Google, Facebook, Amazon, JP Morgan and Sony, and the 2018 ACM SIGKDD Doctoral Dissertation Award. He was named Forbes' Asia 30 Under 30 in 2019.