Papers by Kelvin Lo

2 papers
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (2021.findings-emnlp)

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Challenge: Existing models for text segmentation use supervised and unsupervised learning to perform tasks such as text summarization and keyword extraction.
Approach: They propose a transformer over transformer framework to perform neural text segmentation.
Outcome: The proposed framework outperforms state-of-the-art models in terms of semantic coherence measure . bottom-level sentence encoders pre-trained on specific languages yield better performance .
Explaining Relationships Between Scientific Documents (2021.acl-long)

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Challenge: Existing approaches to explain relationships between scientific documents using natural language text can be useful for research efficiency.
Approach: They propose a task of explaining relationships between scientific documents using natural language text.
Outcome: The proposed models can be automated and humanely evaluated.

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