DesireDB

If you use this data in your research, please refer to and cite: 

Elahe Rahimtoroghi, Jiaqi Wu, Ruimin Wang, Pranav Anand and Marilyn A Walker, "Modelling Protagonist Goals and Desires in First-Person Narrative", Proceedings of 18th Annual SIGDial Meeting on Discourse and Dialouge (SIGDial 2017), Saarbr├╝cken, Germany, 2017.

 

Overview: Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes.  However, to date, there has been limited work on computational models for this problem.    

The Data: We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context.  We  report experiments on tracking desire fulfillment using different methods, and show that our best-performing model achieves F-measure of 0.7 on our corpus. 

 

 

Download: Fill out the following form to download DesireDB Corpus.

Contact: Please direct questions to Elahe Rahimtoroghi: elahe [at] soe [dot] ucsc [dot] edu

Download DesireDB Corpus