Papers with end-to-end nature
Decoupling Strategy and Generation in Negotiation Dialogues (D18-1)
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| Challenge: | Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy. |
| Approach: | They propose a modular approach that decouples strategy and generation by coarse dialogue acts . they test their approach on a recently proposed DEALORNODEAL game . |
| Outcome: | The proposed approach can decouple strategy and generation without degeneracy. |
End-to-end Parsing of Procedural Text into Flow Graphs (2024.lrec-main)
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| Challenge: | Existing flow graph parsers lack sufficient annotated data to train them . a lack of annotation can cause costly training, and poor flow graph training results in a large improvement. |
| Approach: | They propose a multi-task framework that performs tagging and graph generation simultaneously . they take advantage of the abundance of unlabelled recipes and generate noisy silver annotations . |
| Outcome: | The proposed model can unify the input representation and use compact encoders, resulting in small models with significantly fewer parameters than existing models. |