| Challenge: | Existing approaches to improve coherence modeling for paragraphs have been developed. |
| Approach: | They propose a BERT-enhanced Relational Sentence Ordering Network to capture better dependency relationship among sentences and exploit it with a deep relational module. |
| Outcome: | The proposed model shows significant improvement over the state-of-the-art on six datasets. |
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| Challenge: | Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks. |
| Approach: | They propose to combine certain layers of a BERT-based model rested on the data set and model to achieve substantially better results. |
| Outcome: | The proposed method outperforms baseline models on seven semantic textual similarity datasets and on eight transfer data sets. |
Deep Attentive Sentence Ordering Network (D18-1)
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| Challenge: | Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific. |
| Approach: | They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Sentence Ordering and Order Discrimination tasks and is shown to be highly efficient. |
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
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| Challenge: | Existing approaches to extract relationship between entities in sentences suffer from missing or redundant information. |
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| Outcome: | The proposed model outperforms baseline models on the SemEval-2010 dataset. |
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (D19-1)
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| Challenge: | Existing methods for finding similar sentences require multiple inferences . a modern GPU requires 65 hours to find the most similar pair in 10,000 sentences . |
| Approach: | They propose a modification of the pretrained BERT network that uses siamese and triplet networks to derive semantically meaningful sentence embeddings. |
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HEGEL: Hypergraph Transformer for Long Document Summarization (2022.emnlp-main)
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| Challenge: | Abstract: Extractive summarization for long documents is challenging due to the extended structured input context. |
| Approach: | They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations. |
| Outcome: | The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers. |
Sentence Ordering with a Coherence Verifier (2023.findings-acl)
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| Challenge: | Recent sentence ordering studies can be classified into 2 categories: pair-wise ranking-based and sequence generation-based methods. |
| Approach: | They propose a sentence ordering method by plugging a coherence verifier into ranking-based and sequence generation-based methods. |
| Outcome: | The proposed method improves on topological sorting-based and pointer network-based methods with topological and point-based models. |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)
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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
| Challenge: | Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. |
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Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
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| Challenge: | Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. |
| Approach: | They propose to use BERT as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. |
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Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction (2021.acl-short)
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| Challenge: | Document-level relation extraction (RE) is more challenging than sentence RE as it often requires reasoning over multiple sentences. |
| Approach: | They propose a method to heuristically select evidence sentences for document-level relation extraction. |
| Outcome: | The proposed method can be easily combined with BiLSTM to achieve good performance on benchmark datasets even better than fancy graph neural network based methods. |
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information (D19-1)
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| Challenge: | Existing methods for sentence ordering are based on pairwise strategies. |
| Approach: | They propose a topic-guided coherence modeling (TGCM) for sentence ordering that utilizes sentence vectors in a permutation-invariant manner. |
| Outcome: | The proposed model outperforms state-of-the-art models from various perspectives. |