Papers with multi-task architectures

2 papers
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)

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Challenge: Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement.
Approach: They propose novel multi-task architectures with high-level layer-specific sharing across multiple encoder and decoder layers of the three tasks and soft-sharing mechanisms.
Outcome: The proposed model improves on the CNN/DailyMail and Gigaword datasets and on the DUC-2002 transfer setup.
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)

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Challenge: Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders.
Approach: They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks.
Outcome: The proposed architecture does surprisingly well across a range of diverse tasks.

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