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Lent Talklets: Costanza and Cătălina

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  • UserCostanza Conforti (Department of Theoretical and Applied Linguistics, University of Cambridge); Cătălina Cangea (Department of Computer Science and Technology, University of Cambridge)
  • ClockFriday 26 February 2021, 13:00-14:00
  • HouseRemote.

If you have a question about this talk, please contact Agnieszka Slowik.

Speaker 1: Costanza Conforti

Title: NLP -enhanced Sustainable Development: the case of Community Profiling in Rural Uganda

Abstract: In recent years, there has been an increasing interest in the application of AI (and especially Machine Learning) to the field of Sustainable Development (SD). However, until now, NLP has not been systematically applied in this context. In this talk, we discuss the high potential of NLP to enhance community profiling in developing countries. We introduce the new task of Automatic User-Perceived Value classification, and we release an expert-annotated dataset of interviews carried out in rural Uganda. Experimental results show that the problem is challenging, and leaves considerable room for future research at the intersection of NLP and SD.

Speaker 2: Cătălina Cangea

Title: Exploiting multimodality and structure in world representations

Abstract: In this talk, I will give an overview of the major research works I have been involved in during my PhD, which study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of Embodied Question Answering to the visual reasoning problem, along with several models that were evaluated on the novel dataset. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by contemporary best-performing models, or incorporated theoretical properties via the use of topological data analysis algorithms; both competed with state-of-the-art graph classification methods. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot & multi-task learning and scarce labelled-data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline.

This talk is part of the Women@CL Events series.

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