Papers by Christopher Re
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval (2022.findings-acl)
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| Challenge: | Existing methods for entity retrieval use mention boundaries but unstructured text . state-of-the-art methods struggle to retrieve rare entities for ambiguous mentions . |
| Approach: | They propose a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks. |
| Outcome: | The proposed method improves retrieval of rare entities on Ambiguous Entity Retrieval sets while maintaining strong overall retrieval performance on open-domain tasks. |
Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models (2022.findings-acl)
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| Challenge: | Existing methods to capture entity knowledge with factual knowledge are limited . despite its simplicity, metadata shaping is quite effective . |
| Approach: | They propose a method which inserts substrings corresponding to readily available entity metadata into examples at train and inference time based on mutual information. |
| Outcome: | The proposed method exceeds the baseline model by 4.3 F1 points and achieves state-of-the-art results. |