Papers with search procedure
Exploring Chunk Based Templates for Generating a subset of English Text (P18-3)
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| Challenge: | In this paper, we address the problem of the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. |
| Approach: | They propose a fitness function which is used to generate grammatical sentences . they use an evolutionary algorithm to model the fitness of the sentence . |
| Outcome: | The proposed approach uses an evolutionary algorithm to arrive at a potentially grammatical sentence which satisfies the input constraints. |
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations (D18-1)
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| Challenge: | Existing learning approaches for parsing from denotations (SpFD) do not provide access to correct representations, so there are two steps for every training example. |
| Approach: | They propose a framework for parsing from denotations that generalizes three different learning algorithms. |
| Outcome: | The proposed framework outperforms previous work by 5.0% absolute on exact match accuracy on a question answering dataset. |
Natural Language Deduction through Search over Statement Compositions (2022.findings-emnlp)
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| Challenge: | Existing methods focus on the end-to-end discriminative version of this task, but less work has treated the generative version of the task. |
| Approach: | They propose a system that decomposes the task into separate steps coordinated by a search procedure and produces a tree of intermediate conclusions that faithfully reflects the system’s reasoning process. |
| Outcome: | The proposed system proves true statements while rejecting false ones on the EntailmentBank dataset with a 17% absolute higher step validity than the end-to-end T5 model. |
Best-k Search Algorithm for Neural Text Generation (2023.acl-long)
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| Challenge: | Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. |
| Approach: | They propose a deterministic search algorithm balancing quality and diversity . they investigate the vanilla best-first search algorithm and propose k-k search algorithm. |
| Outcome: | The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. |
DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)
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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang
| Challenge: | Recent advances in neural theorem-proving resort to large language models and tree searches. |
| Approach: | They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate. |