Papers with Seq2Seq learning
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction (P19-1)
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| Challenge: | Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence . sequence labeling based methods cannot make full use of overall meaning of sentence if they have dependencies between labels. |
| Approach: | They propose to formalize ATE as a sequence-to-sequence (Seq2Seque) learning task . they propose gated unit networks and position-aware attention mechanism to make it suit to ATE . |
| Outcome: | The proposed learning task is effective when labels correspond to words one by one . the proposed learning system is gated unit networks and position-aware attention mechanism . |
Multi-target Backdoor Attacks for Code Pre-trained Models (2023.acl-long)
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| Challenge: | Existing work for backdoor attacks on neural code models insert triggers into task-specific data for code-related downstream tasks, limiting the scope of attacks. |
| Approach: | They propose task-agnostic backdoor attacks for code pre-trained models . they use two learning strategies to implant backdoors into code understanding and generation models - Poisoned Seq2Seq learning and token representation learning . |
| Outcome: | The proposed model is pre-trained with two learning strategies to support the multi-target attack of downstream code understanding and generation tasks. |