TreeTalk: Composition and Compression of Trees for Image Descriptions

Polina Kuznetsova, Vicente Ordonez, Tamara L. Berg, Yejin Choi


Abstract
We present a new tree based approach to composing expressive image descriptions that makes use of naturally occuring web images with captions. We investigate two related tasks: image caption generalization and generation, where the former is an optional subtask of the latter. The high-level idea of our approach is to harvest expressive phrases (as tree fragments) from existing image descriptions, then to compose a new description by selectively combining the extracted (and optionally pruned) tree fragments. Key algorithmic components are tree composition and compression, both integrating tree structure with sequence structure. Our proposed system attains significantly better performance than previous approaches for both image caption generalization and generation. In addition, our work is the first to show the empirical benefit of automatically generalized captions for composing natural image descriptions.
Anthology ID:
Q14-1028
Volume:
Transactions of the Association for Computational Linguistics, Volume 2
Month:
Year:
2014
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
351–362
Language:
URL:
https://aclanthology.org/Q14-1028
DOI:
10.1162/tacl_a_00188
Bibkey:
Cite (ACL):
Polina Kuznetsova, Vicente Ordonez, Tamara L. Berg, and Yejin Choi. 2014. TreeTalk: Composition and Compression of Trees for Image Descriptions. Transactions of the Association for Computational Linguistics, 2:351–362.
Cite (Informal):
TreeTalk: Composition and Compression of Trees for Image Descriptions (Kuznetsova et al., TACL 2014)
Copy Citation:
PDF:
https://aclanthology.org/Q14-1028.pdf
Data
Sentence Compression