@inproceedings{ghosal-etal-2021-stack,
title = "{ST}a{CK}: Sentence Ordering with Temporal Commonsense Knowledge",
author = "Ghosal, Deepanway and
Majumder, Navonil and
Mihalcea, Rada and
Poria, Soujanya",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.683",
doi = "10.18653/v1/2021.emnlp-main.683",
pages = "8676--8686",
abstract = "Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order. In this paper, we introduce STaCK {---} a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences. Our graph network accumulates temporal evidence using knowledge of {`}past{'} and {`}future{'} and formulates sentence ordering as a constrained edge classification problem. We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction. The implementation of this work is available at: \url{https://github.com/declare-lab/sentence-ordering}.",
}
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<abstract>Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order. In this paper, we introduce STaCK — a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences. Our graph network accumulates temporal evidence using knowledge of ‘past’ and ‘future’ and formulates sentence ordering as a constrained edge classification problem. We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction. The implementation of this work is available at: https://github.com/declare-lab/sentence-ordering.</abstract>
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%0 Conference Proceedings
%T STaCK: Sentence Ordering with Temporal Commonsense Knowledge
%A Ghosal, Deepanway
%A Majumder, Navonil
%A Mihalcea, Rada
%A Poria, Soujanya
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ghosal-etal-2021-stack
%X Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order. In this paper, we introduce STaCK — a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences. Our graph network accumulates temporal evidence using knowledge of ‘past’ and ‘future’ and formulates sentence ordering as a constrained edge classification problem. We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction. The implementation of this work is available at: https://github.com/declare-lab/sentence-ordering.
%R 10.18653/v1/2021.emnlp-main.683
%U https://aclanthology.org/2021.emnlp-main.683
%U https://doi.org/10.18653/v1/2021.emnlp-main.683
%P 8676-8686
Markdown (Informal)
[STaCK: Sentence Ordering with Temporal Commonsense Knowledge](https://aclanthology.org/2021.emnlp-main.683) (Ghosal et al., EMNLP 2021)
ACL
- Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, and Soujanya Poria. 2021. STaCK: Sentence Ordering with Temporal Commonsense Knowledge. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8676–8686, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.