Jessica Huynh


The DialPort tools
Jessica Huynh | Shikib Mehri | Cathy Jiao | Maxine Eskenazi
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

The DialPort project (, funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community. Over the course of six years, several offerings have been created, including the DialPort Portal and DialCrowd. This paper describes these contributions, which will be demoed at SIGDIAL, including implementation, prior studies, corresponding discoveries, and the locations at which the tools will remain freely available to the community going forward.

DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit
Jessica Huynh | Ting-Rui Chiang | Jeffrey Bigham | Maxine Eskenazi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This paper introduces DialCrowd 2.0 to help requesters obtain higher quality data by, for example, presenting tasks more clearly and facilitating effective communication with workers. DialCrowd 2.0 guides developers in creating improved Human Intelligence Tasks (HITs) and is directly applicable to the workflows used currently by developers and researchers.


SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
Steven Y. Feng | Jessica Huynh | Chaitanya Prasad Narisetty | Eduard Hovy | Varun Gangal
Proceedings of the 14th International Conference on Natural Language Generation

We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsense, insufficient specificity, and poor fluency.