Papers with restart-incremental Transformers
TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent approaches for incremental processing use RNNs or Transformers, which consume whole sequences and are by nature non-incremental. |
| Approach: | They propose a two-pass model for AdaPtIve Revision to obtain an incremental supervision signal for learning an adaptive revision policy. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to restart-incremental Transformers while showing little degradation on full sequences. |
When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality (2024.acl-long)
Copied to clipboard
| Challenge: | In incremental models, one interpretation is possible, but models that can revise can do so if the ambiguity is resolved. |
| Approach: | They propose an interpretable way to analyse incremental states in a bidirectional way . they propose to use a model that can update internal states to reflect the garden path effect . |
| Outcome: | The proposed model shows that it can perform revisions and recover if the label is incorrect. |