Topological Sort for Sentence Ordering

Prabhumoye, Shrimai; Salakhutdinov, Ruslan; Black, Alan W.

doi:10.18653/v1/2020.acl-main.248

Cited by 37 publications

(41 citation statements)

References 19 publications

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“…With the advent of deep learning, researchers leveraged distributed sentence representations learned through recurrent neural networks (Li and Hovy, 2014). Recent works adopted rankingbased algorithms to solve the task Kumar et al, 2020;Prabhumoye et al, 2020).…”

Section: Shuffled Inputmentioning

confidence: 99%

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Is Everything in Order? A Simple Way to Order Sentences

Chowdhury¹,

Brahman²,

Chaturvedi³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine's understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional textto-marker generation problem. We present Reorder-BART (RE-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text. RE-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall's tau (τ ). We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform several experiments to understand the functioning and limitations of our framework.

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Section: Shuffled Inputmentioning

confidence: 99%

“…HAN (Wang and Wan, 2019) and TGCM (Oh et al, 2019) used an attention based pointer network for decoding. B-TSort (Prabhumoye et al, 2020) uses topological sorting to retrieve the final order from sentence pairs. Zhu et al ( 2021) encode sentence-level relationships as constraint graphs to enrich sentence representations.…”

Section: Related Workmentioning

confidence: 99%

Is Everything in Order? A Simple Way to Order Sentences

Chowdhury¹,

Brahman²,

Chaturvedi³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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show abstract

“…Additionally, we analyze the displacement of sentences in the predicted orders by calculating the percentage of sentences whose predicted location is within one, two or three positions from their original location (Prabhumoye et al, 2020). The higher score is better, which denotes less displacement of sentences.…”

Section: Sentence Displacement Analysismentioning

confidence: 99%

“…Pairwise Model adopts a pairwise ranking algorithm to learn the relative order of each sentence pair. B-TSort (Prabhumoye et al, 2020) predicts the constraint between two sentences and uses the topological sort technique to find the ordering.…”

Section: Introductionmentioning

confidence: 99%

BERT-enhanced Relational Sentence Ordering Network

Cui¹,

Li²,

Zhang³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In this paper, we introduce a novel BERTenhanced Relational Sentence Ordering Network (referred to as BERSON) by leveraging BERT for capturing a better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph. In particular, we develop a new Relational Pointer Decoder (referred as RPD) by incorporating the relative ordering information into the pointer network with a Deep Relational Module (referred as DRM), which utilizes BERT to exploit the deep semantic connection and relative ordering between sentences. This enables us to strengthen both local and global dependencies among sentences. Extensive evaluations are conducted on six public datasets. The experimental results demonstrate the effectiveness and promise of BERSON, showing a significant improvement over the state-of-the-art by a wide margin. * Corresponding author An unordered set of sentences Coherent paragraph 1 Dan was walking during the night.1 Dan was walking during the night. 3 They tried to steal his book bag.2 A group of thieves surrounded him. 4 A bystander noticed them.3 They tried to steal his book bag. 2 A group of thieves surrounded him. 4 A bystander noticed them. 5 But she continued to walk away.5 But she continued to walk away.

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“…Recently, inspired by the great success of deep learning in other NLP tasks, researchers have resorted to neural sentence ordering models, which can be classified into: pairwise ordering models Agrawal et al, 2016;Li and Jurafsky, 2017;Moon et al, 2019;Kumar et al, 2020;Prabhumoye et al, 2020;Zhu et al, 2021) and set-to-sequence models (Gong et al, 2016;Nguyen and Joty, 2017;Logeswaran et al, 2018;Mohiuddin et al, 2018;Cui et al, 2018;Yin et al, 2019;Oh et al, 2019;Yin et al, 2020;Cui et al, 2020;Yin et al, 2021). Generally, the former predicts the relative orderings between pairwise sentences, which are then leveraged to produce the final ordered sentence sequence.…”

Section: Introductionmentioning

confidence: 99%

Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Lai¹,

Wang²,

Meng³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al., 2019(Yin et al., , 2021. Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted highconfident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model (Yin et al., 2019(Yin et al., , 2021 on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT (Devlin et al., 2019) and FHDecoder (Yin et al., 2020), our model achieves state-of-the-art performance. Our code is available at https:// github.com/DeepLearnXMU/IRSEG.

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Topological Sort for Sentence Ordering

Cited by 37 publications

References 19 publications

Is Everything in Order? A Simple Way to Order Sentences

Is Everything in Order? A Simple Way to Order Sentences

BERT-enhanced Relational Sentence Ordering Network

Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

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