Papers by Hiroyuki Deguchi
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding (2025.acl-long)
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| Challenge: | Existing studies show that MBR decoding improves model generation performance . however, the theoretical underpinnings of these results remain uncertain . |
| Approach: | They propose a theoretical interpretation of MBR decoding from the perspective of bias–diversity decomposition. |
| Outcome: | The proposed method improves the quality estimation of hypotheses by decomposing bias and diversity into two main factors. |
Centroid-Based Efficient Minimum Bayes Risk Decoding (2024.findings-acl)
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| Challenge: | Minimum Bayes risk (MBR) decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations. |
| Approach: | They propose a centroid-based MBR decoding method that clusters the translations in the feature space and calculates the expected score using the centroids of each cluster. |
| Outcome: | The proposed method outperforms vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMT’22 EnJa, EnDe, EnZh, and WMT'23 Enja translation tasks. |
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness (2026.acl-long)
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| Challenge: | et al., 2010) show that hub embeddings are close to many unrelated examples in high-dimensional embeddable spaces . cross-modal encoders that project different modalities into a shared space are useful for cross-module applications . |
| Approach: | They propose a method for identifying the hub embedding and its corresponding hub text . they use images to evaluate cross-modal encoders that project different modalities into a shared space . |
| Outcome: | The proposed method can identify a single hub embedding and its corresponding hub text . it achieves comparable or higher similarity scores than human-written reference captions in many images . |
Synchronous Syntactic Attention for Transformer Neural Machine Translation (2021.acl-srw)
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| Challenge: | Existing syntaxbased NMT models use monolingual syntactic information on either side or both. |
| Approach: | They propose a mechanism that synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target- and target side self- attentions mapped by the encoder-decoder attention matrix. |
| Outcome: | The proposed method improves translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU). |
Long-Tail Crisis in Nearest Neighbor Language Models (2025.findings-naacl)
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| Challenge: | Prior studies have shown that kNN-LM can retrieve long-tail contexts, leaving the model’s performance underexplored in estimating the probabilities of long-tailed target tokens. |
| Approach: | They investigate the behavior of kNN-LM on low-frequency tokens, examining prediction probability, retrieval accuracy, and token distribution in the datastore. |
| Outcome: | The proposed model improves the perplexity of given text by directly accessing a large datastore built from any text data during inference. |
Subset Retrieval Nearest Neighbor Machine Translation (2023.acl-long)
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| Challenge: | k-nearest-neighbor machine translation (kNN-MT) is a new approach to improve NMT performance without additional training. |
| Approach: | They propose a method that integrates example-search into the decoding algorithm to improve neighbor token retrieval. |
| Outcome: | The proposed method achieves a speed-up of up to 132.2 times and an improvement in BLEU score of up 1.6 compared with kNN-MT in the WMT’19 translation task and the domain adaptation tasks in De-En and En-Ja. |
TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency (2026.acl-srw)
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| Challenge: | Experimental results show TableMBR outperforms the baseline, achieving relative improvements of up to 15% in F1 on Rotowire and 23% in accuracy on LiveSum. |
| Approach: | They propose a text-to-table task that generates structured data from unstructured text . they propose 'tableMBR' that maintains structural consistency through minimum Bayes risk decoding . |
| Outcome: | The proposed method outperforms the baseline and achieves relative improvements in F1 and LiveSum. |
Bilingual Subword Segmentation for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing subword segmentation methods tokenize sentences without considering translation . proposed method could be more favorable to machine translation if it uses bilingual sentences . |
| Approach: | They propose a subword segmentation method that tokenizes sentences by using subword units induced from bilingual sentences. |
| Outcome: | The proposed method improves translation performance on translation tasks up to +0.81 BLEU. |
Case-Based Decision-Theoretic Decoding with Quality Memories (2025.emnlp-main)
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| Challenge: | Minimum Bayes risk (MBR) decoding is a decision rule of text generation . however, it depends on sample texts drawn from the text generation model . |
| Approach: | They propose a case-based decision-theoretic method to estimate the expected utility using examples of domain data. |
| Outcome: | The proposed method outperforms MAP decoding in translation tasks and image captioning tasks on MSCOCO and nocaps datasets. |
Hacking Neural Evaluation Metrics with Single Hub Text (2026.eacl-short)
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| Challenge: | Recent embedding-based neural text evaluation metrics are not reliable due to black-box nature of neural networks. |
| Approach: | They propose to find a single adversarial text in the discrete space that is consistently evaluated as high-quality regardless of the test cases. |
| Outcome: | The proposed method outperforms translations generated individually for each source sentence in English-to-Japanese and English- to-German translation tasks. |
mbrs: A Library for Minimum Bayes Risk Decoding (2024.emnlp-demo)
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| Challenge: | Minimum Bayes risk (MBRS) decoding is a decision rule of text generation tasks that outperforms conventional maximum a posteriori (MAP) decoders by selecting high-quality outputs based on quality or preference rather than probability. |
| Approach: | They propose to use minimum bayes risk (MBRS) decoding to determine outputs based on quality rather than probability. |
| Outcome: | MBRS is an MIT-licensed open-source project with a focus on speed, reproducibility, and extensibility. |