Papers by Hirotaka Kameko

5 papers
Annotating Event Appearance for Japanese Chess Commentary Corpus (2020.lrec-1)

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Challenge: Recent studies show that text and non-text data are not always a “true” pair.
Approach: They propose "Event Appearance" labels that show the relationship between events mentioned in texts and those happening in the real world.
Outcome: The proposed labels show the relationship between events mentioned in texts and those happening in the real world.
Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows (2022.coling-1)

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Challenge: a new dataset enables us to learn a cooking action result for each object in a recipe text.
Approach: They propose a multimodal dataset that enables us to learn a cooking action result for each object in a recipe text.
Outcome: The proposed dataset reduces human annotation costs by allowing multimodal information retrieval.
Image Description Dataset for Language Learners (2022.lrec-1)

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Challenge: Language learners are limited by the number of texts or speech they are asked to answer . automatic assessment of image descriptions requires a system that depends on both the learner's native language and the target language.
Approach: They propose a dataset that consists of images, their descriptions, and assessment annotations . they propose 'automatic error correction' task that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence.
Outcome: The proposed model can revise errors that cannot be revised without an image.
Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks (2024.lrec-main)

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Challenge: Existing methods to evaluate geoparsing systems are small-scale and lack coverage of location expressions on general domains.
Approach: They propose a method to construct a large-scale corpus for geoparsing from Wikipedia articles.
Outcome: The proposed method can annotate multiple location expressions with coordinates even with ambiguous expressions.
Annotating Modality Expressions and Event Factuality for a Japanese Chess Commentary Corpus (L18-1)

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Challenge: In recent years, there has been a surge of interest in the natural language processing related to the real world . shogi commentaries are an interesting testbed for these tasks, but can be grounded in the game tree .
Approach: They propose to augment shogi commentaries with game states to generate a game commentary generator.
Outcome: The proposed system can be used to ground symbols and events with factuality . it can be compared with other systems to find out if a commentator is a human .

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