@inproceedings{he-etal-2020-scene,
title = "Scene Graph Modification Based on Natural Language Commands",
author = "He, Xuanli and
Tran, Quan Hung and
Haffari, Gholamreza and
Chang, Walter and
Lin, Zhe and
Bui, Trung and
Dernoncourt, Franck and
Dam, Nhan",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.87",
doi = "10.18653/v1/2020.findings-emnlp.87",
pages = "972--990",
abstract = "Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user{'}s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.",
}
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<abstract>Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user’s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.</abstract>
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%0 Conference Proceedings
%T Scene Graph Modification Based on Natural Language Commands
%A He, Xuanli
%A Tran, Quan Hung
%A Haffari, Gholamreza
%A Chang, Walter
%A Lin, Zhe
%A Bui, Trung
%A Dernoncourt, Franck
%A Dam, Nhan
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F he-etal-2020-scene
%X Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user’s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.
%R 10.18653/v1/2020.findings-emnlp.87
%U https://aclanthology.org/2020.findings-emnlp.87
%U https://doi.org/10.18653/v1/2020.findings-emnlp.87
%P 972-990
Markdown (Informal)
[Scene Graph Modification Based on Natural Language Commands](https://aclanthology.org/2020.findings-emnlp.87) (He et al., Findings 2020)
ACL
- Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Zhe Lin, Trung Bui, Franck Dernoncourt, and Nhan Dam. 2020. Scene Graph Modification Based on Natural Language Commands. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 972–990, Online. Association for Computational Linguistics.