Papers with multi-task learning architectures
Few-Shot and Zero-Shot Learning for Historical Text Normalization (D19-61)
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| Challenge: | Historical text normalization often relies on small training datasets. |
| Approach: | They evaluate 63 multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages. |
| Outcome: | The proposed learning architecture outperforms the simple, but strong identity baseline. |
Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection (C18-1)
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| Challenge: | idioms and metaphors are often studied in isolation, challenging the distinction . e.g., metaphorical concept mappings are ubiquitous in everyday life, thus they are ubiquitous . |
| Approach: | They propose to view the detection problem as a generalized non-literal language classification problem. |
| Outcome: | The proposed model improves on four metaphor and idiom detection tasks in two languages, English and German. |
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. |