Challenge: Japanese is rich in compound verbs consisting of two verbs joined together.
Approach: They built a database of Japanese "Verb + Verb" compounds semi-automatically . they extracted Japanese compound verbs from corpus and found suitable clusters .
Outcome: The proposed database extracts synonymous expressions of Japanese compound verbs from corpus . it then links the results to the "Compound Verb Lexicon"

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LLM-based Literal Example Generation for Japanese Multiword Expressions (2026.acl-srw)

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Challenge: Existing work on Japanese multiword expressions has focused on detecting idiomatic usages in context, leaving literal readings underrepresented.
Approach: They propose to use corpus non-literal usages as contrastive cues for controlled prompting . they compare their results to a test that compares model predictions with human judgments .
Outcome: The proposed model provides more accurate literal examples than prompts that include no hints or literal information.
Simplified Corpus with Core Vocabulary (L18-1)

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Challenge: a study has found that simple Japanese is more accessible to foreigners than English.
Approach: They have constructed a simplified corpus for the Japanese language and selected the core vocabulary.
Outcome: The simplified corpus can be used for automatic text simplification and translating simple Japanese into English and vice-versa.
Word Complexity Estimation for Japanese Lexical Simplification (2020.lrec-1)

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Challenge: Experimental results show that the proposed method achieves the highest performance of Japanese lexical simplification.
Approach: They propose a large-scale word complexity lexicon, a synonym lexicone and a toolkit for developing and benchmarking Japanese lexical simplification systems.
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Construction of a Japanese Word Similarity Dataset (L18-1)

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Challenge: evaluating distributed word representations in languages that do not have such resources is difficult . et al., 2015: distributed word represent a sparse vector indicating the word itself or the context of the word.
Approach: They constructed a Japanese word similarity dataset to evaluate distributed representations in Japanese.
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Creating a Verb Synonym Lexicon Based on a Parallel Corpus (L18-1)

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Challenge: a new lexical resource called CzEngClass is being built to help define synonyms in a bilingual context.
Approach: They propose to group verb senses into bilingual verbal synonym groups and use a parallel dependency corpus to explore semantic 'equivalence' they argue that existence of core argument mappings and adjunct mappings to a common set of semantic roles is a suitable criterion for a reasonable verb synonymy definition .
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Construction of the Corpus of Everyday Japanese Conversation: An Interim Report (L18-1)

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Challenge: a new corpus of everyday conversations is being developed in the field of everyday conversation . the corpus is based on 94 hours of recordings of everyday Japanese conversations .
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Transactions of the Association for Computational Linguistics, Volume 8 (2020.tacl-1)

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Challenge: null
Approach: null
Outcome: null
A Japanese Word Segmentation Proposal (P19-2)

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Challenge: Current word segmentation methods may produce different segmentations for the same strings . this occurs when strings appear in different sentences .
Approach: They propose to use Japanese word segmentation methods that use a morpheme-based approach to produce different segmentations for the same strings.
Outcome: The proposed method produces much more consistent segmentation than the current morpheme-based one.
Creating dialect sub-corpora by clustering: a case in Japanese for an adaptive method (L18-1)

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Challenge: a mixed corpus composed of different dialects is sufficiently resourced to cluster them into dialects.
Approach: They propose a pipeline to derive clusters of dialects from a mixed corpus when their standard counterpart is sufficiently resourced.
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Comparison of Pun Detection Methods Using Japanese Pun Corpus (L18-1)

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Challenge: A sampling survey of typology and component ratio analysis in Japanese puns revealed that the type of Japanese pun that had the largest proportion was a pun type with two sound sequences.
Approach: They propose a method to detect phonetically similar Japanese puns using phonological similarity and insertion / omission of prolonged sounds in addition to lexical feature.
Outcome: The proposed method is validated by adding the rule-based features to the baseline.

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