Papers by Makoto Morishita
MQM-Chat: Multidimensional Quality Metrics for Chat Translation (2025.coling-main)
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| Challenge: | Existing methods for chat translation face challenges due to high levels of ambiguity and stylized contents. |
| Approach: | They propose a multidimensional quality metric for chat translation that includes seven error types . they use human annotations to analyze chat data generated by five translation models . |
| Outcome: | The proposed evaluation metric can qualify errors while highlighting chat-specific issues explicitly. |
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus (2020.lrec-1)
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| Challenge: | Recent machine translation algorithms rely on parallel corpora, but only some resource-rich language pairs can benefit from them. |
| Approach: | They construct a parallel corpus for English-Japanese, which has 8.7 million sentence pairs . they use a web crawler to automatically align parallel sentences in the corpus . |
| Outcome: | The proposed corpus includes a broader range of domains and can be trained with a pre-trained model. |
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms (2026.acl-long)
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Yuto Nishida, Naoki Shikoda, Yosuke Kishinami, Ryo Fujii, Makoto Morishita, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Entity-based QA is a common framework for analyzing non-verbatim memorization, but typically query each entity using a single canonical surface form. |
| Approach: | They propose a dataset that pairs Wikidata factual triples with categorized entity surface forms . they examine surface-conditioned factual memorization and find that prediction outcomes change when only the entity surface form is changed. |
| Outcome: | The proposed dataset shows that large language models memorize factual knowledge when only the subject entity surface form is changed. |
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features (C18-1)
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| Challenge: | Using subwords, we find that the appropriate subword units for the three layers differ depending on the model . incorporating hierarchical subword features improves BLEU scores on the IWSLT evaluation datasets. |
| Approach: | They propose a method that expresses a word by combining "subwords" they propose to incorporate hierarchical subword features into a single embedding layer . |
| Outcome: | The proposed method improves BLEU scores on the IWSLT evaluation datasets. |
NTT Neural Machine Translation Systems at WAT 2019 (D19-52)
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| Challenge: | We submitted two systems for scientific paper subtask and timely disclosure subtask . we evaluated the usefulness of incorporating external data from a wide variety of web pages to improve the translation quality. |
| Approach: | They describe two different translation tasks submitted to WAT 2019 . they submitted scientific paper subtasks and timely disclosure subtask . |
| Outcome: | The proposed system performed better on scientific paper and timely disclosure subtasks. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
| Approach: | They propose to build a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
A Test Set for Discourse Translation from Japanese to English (2020.lrec-1)
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| Challenge: | Compared with a previous study on test sets for English-to-French discourse translation, we needed different approaches because Japanese has zero pronouns and represents different senses in different characters. |
| Approach: | They used a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. |
| Outcome: | The results show that the translation accuracy of Japanese-to-English discourse translation is improved by using context-aware neural machine translation. |
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. |
JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus (2024.lrec-main)
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| Challenge: | a recent study has demonstrated that patent translation accuracy improves as the amount of training data or the number of model parameters increases. |
| Approach: | They construct a bilingual corpus of Japanese-English patent application data from 2000 to 2021 . they extracted 1.4M Japanese- English document pairs and extracted 350M sentence pairs . |
| Outcome: | The proposed method improves translation accuracy by 20 bleu points . it is the first publicly available large-scale Japanese-English patent corpus . |
Context-aware Neural Machine Translation with Mini-batch Embedding (2021.eacl-main)
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| Challenge: | Existing models that translate sentences in sentence-by-sentence manner do not consider the inter-sentent context in a language translation model. |
| Approach: | They propose to use mini-batch embedding to represent the features of sentences in a mini-Batch by choosing sentences from the same document and then embedd it into the model. |
| Outcome: | The proposed method outperforms baseline models and improves writing style or terminology to fit the document’s context. |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks (2026.eacl-long)
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| Challenge: | Automated software engineering is a critical task of software engineers. |
| Approach: | They propose a benchmark to evaluate software migration in real-world Python projects. |
| Outcome: | The proposed benchmark consists of GitHub repositories whose tests fail in response to dependency updates. |
Generating Diverse Translation with Perturbed kNN-MT (2024.eacl-srw)
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| Challenge: | Existing methods to generate multiple translation candidates do not address the overcorrection problem, which discourages the model from generating synonymous expressions and leans toward gold standards, reducing the diversity in the candidates. |
| Approach: | They propose to introduce perturbed k-nearest neighbor machine translation (kNN-MT) to generate more diverse translations. |
| Outcome: | The proposed methods significantly improve candidate diversity and control diversity by tuning the perturbation’s magnitude. |
Domain Adaptation of Machine Translation with Crowdworkers (2022.emnlp-industry)
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| Challenge: | Existing methods for machine translation work well when no in-domain data are available. |
| Approach: | They propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers. |
| Outcome: | The proposed method improves the BLEU scores by an average of +7.8 points compared to a general-purpose translation model. |
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus (2022.lrec-1)
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| Challenge: | Existing parallel corpora for English-Japanese are limited, limiting the accuracy of machine translation models. |
| Approach: | They propose a web-based English-Japanese parallel corpus with 21 million unique sentence pairs . this is more than twice as many as the previous corpus JParaCrawl v2.0 . |
| Outcome: | The proposed corpus boosts the accuracy of machine translation models on various domains. |
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs (2024.findings-acl)
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| Challenge: | Neural machine translation (NMT) systems do not take into account the complexity of the words used to compose the translations. |
| Approach: | They propose a method that replaces high Age of Acquisitions words in translations with simpler words to match the user’s level. |
| Outcome: | The proposed method replaces high-AoA words with lower-Aa words while maintaining high BLEU and COMET scores. |