Abstract The availability of large multilingual pre-trained language models has opened up exciting pathways for developing NLP technologies for languages with scarce resources. In this talk I will advocate for the need to go beyond[…]
Abstract I will present our work on data augmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data, and the task is named[…]
Abstract In recent years, the field of Natural Language Processing has seen a profusion of tasks, datasets, and systems that facilitate reasoning about real-world situations through language (e.g., RTE, MNLI, COMET). Such systems might, for[…]
Abstract Systems that support expressive, situated natural language interactions are essential for expanding access to complex computing systems, such as robots and databases, to non-experts. Reasoning and learning in such natural language interactions is a[…]
Abstract Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of[…]
Abstract We consider a problem of data collection for semantically rich NLU tasks, where detailed semantics of documents (or utterances) are captured using a complex meaning representation. Previously, data collection for such tasks was either[…]
Abstract While the “deep learning tsunami” continues to define the state of the art in speech and language processing, finite-state transducer grammars developed by linguists and engineers are still widely used in industrial, highly-multilingual settings,[…]
