Papers with hierarchical approach

3 papers
Globalizing BERT-based Transformer Architectures for Long Document Summarization (2021.eacl-main)

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Challenge: Existing approaches to fine-tune a large language model on downstream tasks show several limitations when the target task requires to reason with long documents.
Approach: They propose a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers.
Outcome: The proposed approach performs well on three extractive summarization corpora of scientific papers and news articles.
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG (2022.findings-acl)

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Challenge: Existing approaches do not account for the fact that some sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents.
Approach: They propose a hierarchical approach for few-shot and zero-shot generation using a three-moduled jointly trained architecture.
Outcome: The proposed approach achieves state-of-the-art on few-shot and zero-shot settings compared to previous approaches.
A hierarchical approach to vision-based language generation: from simple sentences to complex natural language (2020.coling-main)

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Challenge: Automating video to language translation is a challenging problem, but it is unclear what the neural network learns .
Approach: They propose a hierarchical approach to automatically describing videos in natural language . they propose generating video descriptions as sequences of simple sentences followed by a more complex and fluent description in natural languages.
Outcome: The proposed approach generates video descriptions as sequences of simple sentences, followed by a more complex and fluent description in natural language.

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