Beam search is a powerful technique for finding approximate solutions in structured prediction problems, commonly used in natural language processing, machine translation, and other machine learning applications.
Beam search is an optimization algorithm that explores a search space by maintaining a fixed number of candidate solutions, known as the 'beam.' It iteratively expands the most promising candidates and prunes the less promising ones, eventually converging to an approximate solution. This approach allows for a trade-off between computation time and solution quality by adjusting the beam width parameter.
Recent research has focused on improving the performance and efficiency of beam search. One study proposed learning beam search policies using imitation learning, making the beam an integral part of the model rather than just an artifact of approximate decoding. Another study introduced memory-assisted statistically-ranked beam training for sparse multiple-input multiple-output (MIMO) channels, reducing training overheads in low beam entropy scenarios.
Location-aware beam alignment has also been explored for millimeter wave communication, using location information of user equipment and potential reflecting points to guide the search of future beams. Additionally, researchers have developed a one-step constrained beam search to accelerate recurrent neural network transducer inference by vectorizing multiple hypotheses and pruning redundant search space.
Beam search has been applied to feature selection, outperforming forward selection in cases where features are correlated and have more discriminative power when considered jointly. Furthermore, researchers have proposed best-first beam search, which speeds up the standard implementation of beam search while maintaining similar performance.
In summary, beam search is a versatile and efficient technique for finding approximate solutions in various machine learning applications. Ongoing research continues to enhance its performance, making it an essential tool for developers working with structured prediction problems.
Beam Search
Beam Search Further Reading
1.Learning Beam Search Policies via Imitation Learning http://arxiv.org/abs/1811.00512v2 Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon2.Memory-assisted Statistically-ranked RF Beam Training Algorithm for Sparse MIMO http://arxiv.org/abs/1906.01719v3 Krishan K. Tiwari, Eckhard Grass, John S. Thompson, Rolf Kraemer3.Location-aware Beam Alignment for mmWave Communications http://arxiv.org/abs/1907.02197v1 Orikumhi Igbafe, Jeongwan Kang, Henk Wymeersch, Sunwoo Kim4.Beam Search: Faster and Monotonic http://arxiv.org/abs/2204.02929v1 Sofia Lemons, Carlos Linares López, Robert C. Holte, Wheeler Ruml5.Accelerating RNN Transducer Inference via One-Step Constrained Beam Search http://arxiv.org/abs/2002.03577v1 Juntae Kim, Yoonhan Lee6.Beam Search for Feature Selection http://arxiv.org/abs/2203.04350v1 Nicolas Fraiman, Zichao Li7.A Study of Beam Alignment Based on Coupling Modes in Third Harmonic Superconducting Cavities at FLASH http://arxiv.org/abs/1111.3479v1 P. Zhang, N. Baboi, R. M. Jones8.When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size) http://arxiv.org/abs/1809.00069v1 Liang Huang, Kai Zhao, Mingbo Ma9.Best-First Beam Search http://arxiv.org/abs/2007.03909v5 Clara Meister, Tim Vieira, Ryan Cotterell10.Incremental Beam Manipulation for Natural Language Generation http://arxiv.org/abs/2102.02574v3 James Hargreaves, Andreas Vlachos, Guy EmersonBeam Search Frequently Asked Questions
What is beam search with example?
Beam search is an optimization algorithm used in structured prediction problems to find approximate solutions. It maintains a fixed number of candidate solutions, known as the "beam," and iteratively expands the most promising candidates while pruning the less promising ones. This approach allows for a trade-off between computation time and solution quality by adjusting the beam width parameter. For example, consider a machine translation task where the goal is to translate a sentence from one language to another. Beam search can be used to explore different translations by maintaining a fixed number of partial translations (the beam) and iteratively expanding them by adding new words. The algorithm selects the most promising partial translations based on a scoring function and discards the less promising ones, eventually converging to an approximate solution.
What is beam search in NLP?
In natural language processing (NLP), beam search is commonly used for tasks such as machine translation, speech recognition, and text generation. It helps find the most likely sequence of words or tokens given a model's predictions. By maintaining a fixed number of candidate sequences (the beam) and iteratively expanding them, beam search balances the trade-off between computation time and solution quality, providing more accurate results than greedy search methods.
What is beam vs greedy search?
Beam search and greedy search are both optimization algorithms used to find approximate solutions in structured prediction problems. The main difference between them lies in their exploration strategy: - Greedy search selects the best candidate at each step, making a locally optimal choice without considering the global context. This approach can lead to suboptimal solutions, as it may get stuck in local optima. - Beam search, on the other hand, maintains a fixed number of candidate solutions (the beam) and iteratively expands the most promising ones while pruning the less promising ones. This allows for a more global exploration of the search space, resulting in better approximate solutions at the cost of increased computation time.
What is word beam search?
Word beam search is a variant of beam search specifically designed for optical character recognition (OCR) and handwriting recognition tasks. It incorporates a language model to guide the search process, favoring sequences of characters that form valid words in the target language. This approach improves the accuracy of the recognition process by leveraging the linguistic structure of the text, resulting in more coherent and meaningful output.
How does beam search work in machine translation?
In machine translation, beam search is used to find the most likely translation of a given source sentence. The algorithm maintains a fixed number of partial translations (the beam) and iteratively expands them by adding new words based on the model's predictions. At each step, the most promising partial translations are selected based on a scoring function, and the less promising ones are discarded. The process continues until a stopping criterion is met, such as reaching the maximum sentence length or finding a translation with a high enough score.
How do you choose the beam width in beam search?
The beam width is a crucial parameter in beam search, as it determines the number of candidate solutions maintained at each step of the algorithm. A larger beam width allows for a more extensive exploration of the search space, potentially leading to better approximate solutions but at the cost of increased computation time. Conversely, a smaller beam width reduces computation time but may result in suboptimal solutions. Choosing the optimal beam width depends on the specific problem and the available computational resources. In practice, it is common to perform a parameter search or use cross-validation to find a suitable beam width that balances the trade-off between solution quality and computation time.
What are the limitations of beam search?
Beam search has some limitations, including: 1. Suboptimality: Beam search provides approximate solutions, which may not be globally optimal. The quality of the solution depends on the beam width and the scoring function used. 2. Computation time: Beam search can be computationally expensive, especially for large beam widths or complex search spaces. This may limit its applicability in real-time or resource-constrained scenarios. 3. Sensitivity to beam width: The performance of beam search is highly dependent on the choice of beam width, which can be challenging to determine for a specific problem. 4. Local optima: Beam search may get stuck in local optima, especially when the search space is large or the scoring function is not well-suited to the problem.
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