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    <abstract>Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.</abstract>
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%0 Conference Proceedings
%T Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
%A Yang, Pengcheng
%A Zhang, Zhihan
%A Luo, Fuli
%A Li, Lei
%A Huang, Chengyang
%A Sun, Xu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yang-etal-2019-cross
%X Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.
%R 10.18653/v1/P19-1257
%U https://aclanthology.org/P19-1257
%U https://doi.org/10.18653/v1/P19-1257
%P 2680-2686

Markdown (Informal)

[Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information](https://aclanthology.org/P19-1257) (Yang et al., ACL 2019)

• Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information (Yang et al., ACL 2019)

ACL

• Pengcheng Yang, Zhihan Zhang, Fuli Luo, Lei Li, Chengyang Huang, and Xu Sun. 2019. Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2680–2686, Florence, Italy. Association for Computational Linguistics.