GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution

Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal


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 https://github.com/alexa/gravl-bert.
Anthology ID:
2022.coling-1.22
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
285–297
Language:
URL:
https://aclanthology.org/2022.coling-1.22
DOI:
Bibkey:
Cite (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.
Cite (Informal):
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (Guo et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.22.pdf
Code
 alexa/gravl-bert