Zero-Shot Compositional Concept Learning

Guangyue Xu, Parisa Kordjamshidi, Joyce Chai


Abstract
In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more indepth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the ransductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.
Anthology ID:
2021.metanlp-1.3
Volume:
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | MetaNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–27
Language:
URL:
https://aclanthology.org/2021.metanlp-1.3
DOI:
10.18653/v1/2021.metanlp-1.3
Bibkey:
Cite (ACL):
Guangyue Xu, Parisa Kordjamshidi, and Joyce Chai. 2021. Zero-Shot Compositional Concept Learning. In Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing, pages 19–27, Online. Association for Computational Linguistics.
Cite (Informal):
Zero-Shot Compositional Concept Learning (Xu et al., MetaNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.metanlp-1.3.pdf
Data
MIT-States