Papers with hierarchical transformer architecture

2 papers
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)

Copied to clipboard

Challenge: Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity.
Approach: They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture.
Outcome: The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity.
Modelling Temporal Document Sequences for Clinical ICD Coding (2023.eacl-main)

Copied to clipboard

Challenge: Existing studies on the ICD coding task focus on extracting codes from the discharge summary, but there is potential to automate the task by identifying relevant information from clinical notes.
Approach: They propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding.
Outcome: The proposed model exceeds the state-of-the-art when using only discharge summaries as input and achieves performance improvements when all clinical notes are used as input.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations