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UBC Theses and Dissertations

From neural discourse parsing to content structuring : towards a large-scale data-driven approach to discourse processing Guz, Grigorii Anatolievich


In this thesis, we propose novel approaches for supervised RST-style discourse parsing, as well as the methods for utilizing those discourse structures for the benefit of natural language generation. We demonstrate a significant improvement in discourse parsing accuracy on RST-DT and Instr-DT treebanks by incorporating silver-standard supervision. Furthermore, in line with theoretical and empirical connections between the discourse parsing and coreference resolution tasks, we find the evidence of improvement of discourse parsing accuracy on RST-DT when our proposed discourse parsing system is provided with coreference supervision from a coreference resolver trained on OntoNotes corpus. Finally, in extending our work to natural language generation, we demonstrate that our novel content structuring system utilizing silver-standard discourse structures outperforms text-only systems on our proposed task of elementary discourse unit ordering, a significantly more difficult version of sentence ordering task.

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