Justin Lewis


CONSISTENT: Open-Ended Question Generation From News Articles
Tuhin Chakrabarty | Justin Lewis | Smaranda Muresan
Findings of the Association for Computational Linguistics: EMNLP 2022

Recent work on question generation has largely focused on factoid questions such as who, what,where, when about basic facts. Generating open-ended why, how, what, etc. questions thatrequire long-form answers have proven more difficult. To facilitate the generation of openended questions, we propose CONSISTENT, a new end-to-end system for generating openended questions that are answerable from and faithful to the input text. Using news articles asa trustworthy foundation for experimentation, we demonstrate our model’s strength over several baselines using both automatic and human based evaluations. We contribute an evaluationdataset of expert-generated open-ended questions. We discuss potential downstream applications for news media organizations.


Learning Biological Processes with Global Constraints
Aju Thalappillil Scaria | Jonathan Berant | Mengqiu Wang | Peter Clark | Justin Lewis | Brittany Harding | Christopher D. Manning
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing