Paper Info
Title
Multi-Task Video Captioning With Video And Entailment Generation
Abstract
Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailing caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.
Year
DOI
Venue
2017
10.18653/v1/P17-1117
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1
DocType
Volume
Citations 
Conference
abs/1704.07489
21
PageRank 
References 
Authors
0.75
28
2
Name
Order
Citations
PageRank
Ramakanth Pasunuru1253.69
Mohit Bansal287163.19