Name
Abstract | ||
|---|---|---|
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. 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 LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/w17-5543 | SIGDIAL Conference |
DocType | Volume | Citations |
Conference | abs/1708.09040 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elahe Rahimtoroghi | 1 | 12 | 2.64 |
| Wu Jiaqi | 2 | 20 | 7.16 |
| Ruimin Wang | 3 | 156 | 9.18 |
| Pranav Anand | 4 | 260 | 19.70 |
| Marilyn A Walker | 5 | 3893 | 418.91 |