Papers with Chinese large-scale

2 papers
Improving Embedding-based Large-scale Retrieval via Label Enhancement (2021.findings-emnlp)

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Challenge: Existing methods for large-scale retrieval are trained with 0-1 hard labels that indicate whether a query is relevant to a document, ignoring rich information of the relevance degree.
Approach: They propose to introduce label enhancement for the first time to characterize query-document relevance degree by embedding label distribution into contextual embeddables.
Outcome: The proposed method significantly outperforms existing retrieval models and its counterparts equipped with two alternative methods on English and Chinese large-scale retrieval tasks.
MovieUN: A Dataset for Movie Understanding and Narrating (2022.findings-emnlp)

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Challenge: Automatic movie narration generation and narration grounding are important to provide a true movie experience for the blind and visually impaired.
Approach: They propose to use movie clips as a benchmark to support automatic movie narration generation and narration grounding tasks.
Outcome: The proposed methods are effective in supporting two movie-based tasks for the blind and visually impaired.

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