| Challenge: | Existing corpora focus on emotional expressions in conversations, but there are no large-scale corpors focusing on the relationships between emotions and utterances. |
| Approach: | They propose a Japanese Feature Change Knowledge Base (JFCKB) that focuses on emotional expressions in conversations. |
| Outcome: | The proposed corpus can recognize reasonableness of a given conversation. |
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JFCKB: Japanese Feature Change Knowledge Base (L18-1)
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| 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. |
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Construction of the Corpus of Everyday Japanese Conversation: An Interim Report (L18-1)
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Hanae Koiso, Yasuharu Den, Yuriko Iseki, Wakako Kashino, Yoshiko Kawabata, Ken’ya Nishikawa, Yayoi Tanaka, Yasuyuki Usuda
| 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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Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions (2022.acl-srw)
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| Challenge: | a human would recognize the emotion of an interlocutor and respond with an appropriate emotion, such as empathy and comfort. |
| Approach: | They propose to build a dialogue corpus annotated with two kinds of emotions . they collect tweets and annotate them with the emotion they put into the utterance . |
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JESC: Japanese-English Subtitle Corpus (L18-1)
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| Challenge: | Existing data on Japanese-English subtitles are limited due to the high cost of manual construction. |
| Approach: | They describe the Japanese-English Subtitle Corpus by crawling and aligning subtitles found on the web. |
| Outcome: | The JESC dataset covers the underrepresented domain of conversational dialogue. |
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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The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
Self-Contained Utterance Description Corpus for Japanese Dialog (2022.lrec-1)
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| Challenge: | Existing task frameworks for dialog-act classification and slot filling can only interpret utterances using pre-defined types and slots. |
| Approach: | They propose a task to describe the intent of an utterance in a dialog with multiple simple natural sentences without the context. |
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JCoLA: Japanese Corpus of Linguistic Acceptability (2024.lrec-main)
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| Challenge: | Neural language models have exhibited outstanding performance in downstream tasks, yet there is limited understanding regarding the extent of their internalization of syntactic knowledge. |
| Approach: | They introduce a dataset that analyzes sentences annotated with binary acceptability judgments from linguistic textbooks and handbooks and splits them into in-domain and out-of-domain data. |
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A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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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. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization (2022.emnlp-main)
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| Challenge: | e-commerce users express their needs using text, images, or videos . but detailed information provided by images is limited, and customer service systems cannot understand the intent of users without the input text. |
| Approach: | They construct a large-scale multimodal multi-turn dialogue dataset from a mainstream Chinese E-commerce platform . the dataset contains about 246K dialogue sessions, 3M utterances, and 507K images . |
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