| Challenge: | constructing commonsense knowledge including connotational meanings is challenging . a recent study focused on denotation and connotations, but few studies focused on connotating meanings . |
| Approach: | They propose to construct a Japanese knowledge base where arguments in event sentences are associated with feature changes caused by events. |
| Outcome: | The proposed knowledge base is able to generate anaphora resolution tasks in Japanese . it is useful for computers to understand texts, but it is difficult to acquire it . |
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
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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 . |
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
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JGLUE: Japanese General Language Understanding Evaluation (2022.lrec-1)
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| Challenge: | There is no benchmark for Japanese to evaluate and analyze NLU ability from different perspectives. |
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Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations (L18-1)
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Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
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BERT-based Cohesion Analysis of Japanese Texts (2020.coling-main)
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| Challenge: | Recent advances in neural networks have significantly improved natural language processing tasks . they include self training-based language models such as BERT . |
| Approach: | They tackle a systematic analysis of cohesion in Japanese texts using BERT models . they find that coreference resolution is different in nature from other tasks . |
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