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. |
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