@inproceedings{xu-etal-2021-zero,
title = "Zero-Shot Compositional Concept Learning",
author = "Xu, Guangyue and
Kordjamshidi, Parisa and
Chai, Joyce",
editor = "Lee, Hung-Yi and
Mohtarami, Mitra and
Li, Shang-Wen and
Jin, Di and
Korpusik, Mandy and
Dong, Shuyan and
Vu, Ngoc Thang and
Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.metanlp-1.3",
doi = "10.18653/v1/2021.metanlp-1.3",
pages = "19--27",
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.",
}
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%0 Conference Proceedings
%T Zero-Shot Compositional Concept Learning
%A Xu, Guangyue
%A Kordjamshidi, Parisa
%A Chai, Joyce
%Y Lee, Hung-Yi
%Y Mohtarami, Mitra
%Y Li, Shang-Wen
%Y Jin, Di
%Y Korpusik, Mandy
%Y Dong, Shuyan
%Y Vu, Ngoc Thang
%Y Hakkani-Tur, Dilek
%S Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F xu-etal-2021-zero
%X 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.
%R 10.18653/v1/2021.metanlp-1.3
%U https://aclanthology.org/2021.metanlp-1.3
%U https://doi.org/10.18653/v1/2021.metanlp-1.3
%P 19-27
Markdown (Informal)
[Zero-Shot Compositional Concept Learning](https://aclanthology.org/2021.metanlp-1.3) (Xu et al., MetaNLP 2021)
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.