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Transactions of the Association for Computational Linguistics, 2022 Decoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search because the problem of searching the full output space is often intractable, or impractical in many settings. The default algorithm for this job is beam search—a pruned version of breadth-first search.
Clara Meister +2 more
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Beam Search: Faster and Monotonic [PDF]
Proceedings of the International Conference on Automated Planning and Scheduling, 2022 Beam search is a popular satisficing approach to heuristic search problems that allows one to trade increased computation time for lower solution cost by increasing the beam width parameter. We make two contributions to the study of beam search. First, we show how to make beam search monotonic; that is, we provide a new variant that guarantees ...
Sofia Lemons +3 more
semanticscholar +5 more sources
Determinantal Beam Search [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word.
Meister, Clara Isabel; id_orcid0000-0002-3775-4426 +2 more
semanticscholar +6 more sources
A Beam Search Framework for Quantum Circuit Mapping [PDF]
EntropyIn the era of noisy intermediate-scale quantum (NISQ) computing, the limited connectivity between qubits is one of the common physical limitations faced by current quantum computing devices. Quantum circuit mapping methods transform quantum circuits into
Cheng Qiu, Pengcheng Zhu, Lihua Wei
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Meshed Context-Aware Beam Search for Image Captioning [PDF]
EntropyBeam search is a commonly used algorithm in image captioning to improve the accuracy and robustness of generated captions by finding the optimal word sequence. However, it mainly focuses on the highest-scoring sequence at each step, often overlooking the
Fengzhi Zhao +3 more
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A Fully Differentiable Beam Search Decoder [PDF]
, 2019 We introduce a new beam search decoder that is fully differentiable, making it possible to optimize at training time through the inference procedure. Our decoder allows us to combine models which operate at different granularities (e.g. acoustic and language models). It can be used when target sequences are not aligned to input sequences by considering
Ronan Collobert +2 more
openalex +3 more sources
Light Pentaquark Searches with Hadron Beams
Acta Physica Polonica BThis review deals with measurements and future experiments of light pentaquark searches using hadron beams. Abstract Published by the Jagiellonian University 2025 authors
J. K. Ahn
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Decoding Methods in Neural Language Generation: A Survey
Information, 2021 Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding ...
Sina Zarrieß +2 more
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A Combined Extractive With Abstractive Model for Summarization
IEEE Access, 2021 Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used ...
Wenfeng Liu +3 more
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Entropy-Based Dynamic Rescoring with Language Model in E2E ASR Systems
Applied Sciences, 2022 Language models (LM) have played crucial roles in automatic speech recognition (ASR), whether as an essential part of a conventional ASR system composed of an acoustic model and LM, or as an integrated model to enhance the performance of novel end-to-end
Zhuo Gong +2 more
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