| Challenge: | Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial. |
| Approach: | They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model. |
| Outcome: | The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search. |
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A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
Machine Translation Decoding beyond Beam Search (2021.emnlp-main)
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Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Lespiau Jean-Baptiste, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals
| Challenge: | a new study examines whether beam search can be replaced by a more powerful metric-driven search technique. |
| Approach: | They propose a beam search method which is agnostic to the end metric and report results on a variety of metrics. |
| Outcome: | The proposed method is based on a Monte-Carlo Tree Search (MCTS) based method and shows it can be used in language applications. |
If beam search is the answer, what was the question? (2020.emnlp-main)
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| Challenge: | surprisingly, beam search results on language generation tasks are low-quality . despite its high error rate, beam searches can be used to decode models with high probability . |
| Approach: | They frame beam search as the exact solution to a different decoding objective . they propose a set of decoding objectives that explicitly enforce this property . |
| Outcome: | The proposed method enforces uniform information density in text, a property motivated by cognitive science. |
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation (P18-2)
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| Challenge: | a beam search algorithm produces monotonic left-to-right order, meaning a hypothesis cannot be revisited . a proposed algorithm allows discarded hypotheses to be recovered in a later step. |
| Approach: | They propose to decode a beam search algorithm that considers multiple hypotheses simultaneously . they propose to maintain all found hypothese a single priority queue and a universal score function . |
| Outcome: | The proposed algorithm improves translations even for high-performance models in English-Japanese translation task. |
Learning to Stop in Structured Prediction for Neural Machine Translation (N19-1)
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| Challenge: | Beam search optimization solves many problems in neural machine translation, but lacks principled stopping criteria and does not learn how to stop during training. |
| Approach: | They propose a ranking method which enables an optimal beam search stop-ping criteria and a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. |
| Outcome: | Experiments on synthetic and real languages show that the proposed methods improve translation quality and length. |
Incremental Beam Manipulation for Natural Language Generation (2021.eacl-main)
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| Challenge: | a larger beam size can lead to deteriorating performance of natural language generation systems due to model errors . performance of NLG systems can plateau or even decrease when beam sizes larger than 10 are used . |
| Approach: | They propose to rerank the output of beam search to produce a good set of hypotheses . they propose incremental beam manipulation to discarded hypothese . |
| Outcome: | The proposed method outperforms a strong reranker on the E2E and WebNLG datasets while being on par with the existing method. |
On Hallucination and Predictive Uncertainty in Conditional Language Generation (2021.eacl-main)
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| Challenge: | Modern deep neural network models have brought drastic improvements in generation quality measured by standard metrics on different natural language generation tasks. |
| Approach: | They propose a beam search extension to reduce hallucination in conditional language generation by adding a prediction extension to beam search. |
| Outcome: | The proposed extension improves trading performance on standard metric for less hallucination with the proposed beam search variant. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
First the Worst: Finding Better Gender Translations During Beam Search (2022.findings-acl)
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| Challenge: | Neural language generation models optimized by likelihood tend towards 'safe' word choice. |
| Approach: | They propose to use beam search to improve gender diversity in n-best lists and rerank n best lists using gender features obtained from the source sentence to address this problem. |
| Outcome: | The proposed approach improves gender diversity in n-best lists and reranks n best lists using gender features obtained from the source sentence. |
A Streaming Approach For Efficient Batched Beam Search (2020.emnlp-main)
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| Challenge: | During decoding, candidates terminate or are pruned according to heuristics, a streaming method is used to "refill" the batch after it finishes translating some fraction of the current batch. |
| Approach: | They propose an efficient batching strategy for variable-length decoding on GPU architectures by streamlining the batching process. |
| Outcome: | The proposed method reduces runtime by 71% compared to a fixed-width beam search baseline and 17% compared with a variable-widness baseline while matching baselines’ BLEU. |