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Hiroyuki Deguchi

7 accepted papers

2025

Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding

ACL 2025long

Inference methods play an important role in eliciting the performance of large language models (LLMs). Currently, LLMs use inference methods utilizing generated multiple samples, which can be derived from Minimum Bayes Risk (MBR) Decoding. Previous studies have conducted empirical analyses to clarif…

2025

Long-Tail Crisis in Nearest Neighbor Language Models

NAACL 2025findings

The k-nearest-neighbor language model (kNN-LM), one of the retrieval-augmented language models, improves the perplexity for given text by directly accessing a large datastore built from any text data during inference.A widely held hypothesis for the success of kNN-LM is that its explicit memory, i.e…

2025

SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches

ICLR 2025poster

Researchers and practitioners in natural language processing and computational linguistics frequently observe and analyze the real language usage in large-scale corpora. For that purpose, they often employ off-the-shelf pattern-matching tools, such as grep, and keyword-in-context concordancers, whic…

Cited by 0SourcePDFScholar
2024

Centroid-Based Efficient Minimum Bayes Risk Decoding

ACL 2024findings

Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation.However, MBR decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference…

2023

Subset Retrieval Nearest Neighbor Machine Translation

ACL 2023long

k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021) boosts the translation performance of trained neural machine translation (NMT) models by incorporating example-search into the decoding algorithm. However, decoding is seriously time-consuming, i.e., roughly 100 to 1,000 times…

Cited by 18SourcePDFScholar
2020

Bilingual Subword Segmentation for Neural Machine Translation

COLING 2020main

This paper proposed a new subword segmentation method for neural machine translation, “Bilingual Subword Segmentation,” which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods toke…