Papers by Baiyun Cui
BERT-enhanced Relational Sentence Ordering Network (2020.emnlp-main)
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| 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. |
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. |
Fine-tune BERT with Sparse Self-Attention Mechanism (D19-1)
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| Challenge: | Existing sparse self-attention fine-tuning models have been used to improve sentiment analysis, question answering, and natural language inference tasks. |
| Approach: | They propose a Sparse Self-Attention Fine-tuning model which integrates sparsity into self-attention mechanism to enhance the fine-tune performance of BERT. |
| Outcome: | The proposed model outperforms the baseline models on sentiment analysis, question answering, and natural language inference tasks and is able to interpret the input better. |