Leveraging more accurate and flexible discourse structures in question-answering and summarization

Existing systems for critical NLP tasks like question-answering and summarization are still unable to accurately uncover and effectively leverage the discourse structure of text; i.e., how clauses and sentences are related to each other in a document. This is a serious limitation in that relationships between clauses and sentences carries important information, which allows the text to express a meaning as a whole, beyond the sum of its parts. The goal of discourse parsing is to automatically determine the coherence structure of text. In essence, a discourse parser takes a document as input and returns its discourse structure, or tree, showing how clauses and sentences are related to each other, via the use of various discourse relations. In this project, Dr. Carenini's team seek to improve discourse parsing performance and to apply discourse parsing outputs to improve the performance of other NLP tasks, with a specific focus on state-of-the-art approaches to Q&A systems and text summarization.

Trainees: Patrick Huber (PhD candidate), Wen Xiao (MSc candidate)

This project is sponsored by the DSI-Huawei Research Program