Papers with hierarchical transformer

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

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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.
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)

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Challenge: Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences.
Approach: They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively.
Outcome: The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations.

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