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Modelling syntactico-semantic composition for natural language understanding and generation

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  • UserDr Weiwei Sun - Department of Computer Science and Technology, University of Cambridge
  • ClockWednesday 10 March 2021, 15:00-16:00
  • HouseOnline.

If you have a question about this talk, please contact Ben Karniely.

One of the central problems in language technology is the realization of an accurate mapping between natural language utterances and in-depth meaning representations. In this talk, I will discuss methods for this bi-directional mapping which exploit graph-centric representations, formalisms, algorithms and neural networks. I will first introduce a neural graph rewriting framework to model syntactico-semantic composition which combines the strengths of Hyperedge Replacement Grammar (HRG), for knowledge-intensive learning, and Graph Neural Networks, for data-intensive learning. I will then discuss two fundamental problems in this framework: semantic graph parsing and parsing semantic graphs, i.e. computing all possible / the best derivation(s) of a given string or graph. In particular, I will demonstrate that exact graph parsing can be efficient for large graphs and with large grammars. With the ability to enumerate every derivation of a surface string or a semantic graph, we are ready to build practical language understanding and generation systems. I will report on neural systems that achieve state-of-the-art accuracy for English Resource Semantics. I will conclude by laying out a few ideas for future work on meaning representation—mediated Natural Language Processing.

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This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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