Papers with depth-first search
Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
| Approach: | They propose a stack-pointer network-based dependency parser that reads whole sentence and builds dependency tree top-down in a depth-first fashion. |
| Outcome: | The proposed model reads and encodes whole sentence, then builds dependency tree top-down (from root-to-leaf) in a depth-first fashion. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
On NMT Search Errors and Model Errors: Cat Got Your Tongue? (D19-1)
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| Challenge: | We show that at the root of the problem of empty translations lies an inherent bias towards shorter translations. |
| Approach: | They propose an exact inference procedure for neural sequence models based on beam search and depth-first search. |
| Outcome: | The proposed procedure finds that beam search fails to find the best model scores . the results show that the model often prefers an empty translation . |
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints (D19-1)
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| Challenge: | Existing methods for creating anagrams do not pay much attention to the naturalness of the generated anagramms. |
| Approach: | They propose to combine depth-first search with modern neural language models to generate anagrams by permutation of characters in an input sentence or phrase. |
| Outcome: | The proposed method generates significantly more natural anagrams than baseline methods. |