QUDSELECT: Selective Decoding for Questions Under Discussion Parsing

Ashima Suvarna, Xiao Liu, Tanmay Parekh, Kai-Wei Chang, and Nanyun Peng, in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), short, 2024.

Abstract

Question Under Discussion (QUD) is a discourse framework that uses implicit questions to reveal discourse relationships between sentences. In QUD parsing, each sentence is viewed as an answer to a question triggered by an anchor sentence in prior context. The resulting QUD structure is required to conform to several theoretical criteria, making QUD parsing a challenging task. We introduce QUDSELECT, a joint-training framework that selectively decodes the QUD dependency structures considering the QUD criteria. Our method outperforms state-of-the-art baseline models by 9% in human evaluation and 4% in automatic evaluation, demonstrating the effectiveness of our framework.

Bib Entry

@inproceedings{suvarna2024qudselect,
  title = {QUDSELECT: Selective Decoding for Questions Under Discussion Parsing},
  author = {Suvarna, Ashima and Liu, Xiao and Parekh, Tanmay and Chang, Kai-Wei and Peng, Nanyun},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), short},
  year = {2024}
}