Papers with Policy Learning
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |
Experience as Source for Anticipation and Planning: Experiential Policy Learning for Target-driven Recommendation Dialogues (2024.findings-emnlp)
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| Challenge: | Existing approaches to enhance dialogues lack the ability to anticipate user interactions . current approaches lack the capability to anticipate past interactions and to neglect past experiences . |
| Approach: | They propose a framework for enhancing dialogue anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. |
| Outcome: | Experiments on two datasets show the framework is superior and effective . tree-structured EPL assesses past dialogue states with LLMs and MCTS . |