Sentence Ordering
21 papers with code • 1 benchmarks • 2 datasets
Sentence ordering task deals with finding the correct order of sentences given a randomly ordered paragraph.
Benchmarks
These leaderboards are used to track progress in Sentence Ordering
| Trend | Dataset | Best Model | Paper | Code | Compare |
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OrdinalDataset
Most implemented papers
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
shrimai/Topological-Sort-for-Sentence-Ordering
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Modeling the structure of coherent texts is a key NLP problem.
Topological Sort for Sentence Ordering
shrimai/Topological-Sort-for-Sentence-Ordering
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ACL 2020
Sentence ordering is the task of arranging the sentences of a given text in the correct order.
InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?
Sentence insertion is an interesting NLP problem but received insufficient attention.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Text Coherence Analysis Based on Deep Neural Network
In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence.
Partially Shuffling the Training Data to Improve Language Models
ofirpress/PartialShuffle
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arXiv 2019
Although SGD requires shuffling the training data between epochs, currently none of the word-level language modeling systems do this.
Graph-based Neural Sentence Ordering
Sentence ordering is to restore the original paragraph from a set of sentences.
On Losses for Modern Language Models
BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized.
Neural Sentence Ordering Based on Constraint Graphs
Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.
Is Everything in Order? A Simple Way to Order Sentences
fabrahman/rebart
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EMNLP 2021
We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.
