@inproceedings{guo-etal-2022-gravl,
title = "{GRAVL}-{BERT}: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution",
author = "Guo, Danfeng and
Gupta, Arpit and
Agarwal, Sanchit and
Kao, Jiun-Yu and
Gao, Shuyang and
Biswas, Arijit and
Lin, Chien-Wei and
Chung, Tagyoung and
Bansal, Mohit",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.22",
pages = "285--297",
abstract = "Learning from multimodal data has become a popular research topic in recent years. Multimodal coreference resolution (MCR) is an important task in this area. MCR involves resolving the references across different modalities, e.g., text and images, which is a crucial capability for building next-generation conversational agents. MCR is challenging as it requires encoding information from different modalities and modeling associations between them. Although significant progress has been made for visual-linguistic tasks such as visual grounding, most of the current works involve single turn utterances and focus on simple coreference resolutions. In this work, we propose an MCR model that resolves coreferences made in multi-turn dialogues with scene images. We present GRAVL-BERT, a unified MCR framework which combines visual relationships between objects, background scenes, dialogue, and metadata by integrating Graph Neural Networks with VL-BERT. We present results on the SIMMC 2.0 multimodal conversational dataset, achieving the rank-1 on the DSTC-10 SIMMC 2.0 MCR challenge with F1 score 0.783. Our code is available at \url{https://github.com/alexa/gravl-bert}.",
}
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%0 Conference Proceedings
%T GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
%A Guo, Danfeng
%A Gupta, Arpit
%A Agarwal, Sanchit
%A Kao, Jiun-Yu
%A Gao, Shuyang
%A Biswas, Arijit
%A Lin, Chien-Wei
%A Chung, Tagyoung
%A Bansal, Mohit
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F guo-etal-2022-gravl
%X Learning from multimodal data has become a popular research topic in recent years. Multimodal coreference resolution (MCR) is an important task in this area. MCR involves resolving the references across different modalities, e.g., text and images, which is a crucial capability for building next-generation conversational agents. MCR is challenging as it requires encoding information from different modalities and modeling associations between them. Although significant progress has been made for visual-linguistic tasks such as visual grounding, most of the current works involve single turn utterances and focus on simple coreference resolutions. In this work, we propose an MCR model that resolves coreferences made in multi-turn dialogues with scene images. We present GRAVL-BERT, a unified MCR framework which combines visual relationships between objects, background scenes, dialogue, and metadata by integrating Graph Neural Networks with VL-BERT. We present results on the SIMMC 2.0 multimodal conversational dataset, achieving the rank-1 on the DSTC-10 SIMMC 2.0 MCR challenge with F1 score 0.783. Our code is available at https://github.com/alexa/gravl-bert.
%U https://aclanthology.org/2022.coling-1.22
%P 285-297
Markdown (Informal)
[GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution](https://aclanthology.org/2022.coling-1.22) (Guo et al., COLING 2022)
ACL
- Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, and Mohit Bansal. 2022. GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution. In Proceedings of the 29th International Conference on Computational Linguistics, pages 285–297, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.