Abstract Voice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one’s voice to sound like that of another without changing the linguistic content. Voice conversion belongs[…]

Read More

Abstract Modern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However, by and large, such models store and use knowledge in distributed and[…]

Read More

Abstract Recursive calls over recursive data are widely useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful,[…]

Read More

Abstract Model robustness and spurious correlations have received increasing attention in the NLP community, both in methods and evaluation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by[…]

Read More

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[…]

Read More

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[…]

Read More

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[…]

Read More