| Challenge: | XEL is challenging for most languages because of limited availability of requisite resources . simulated environments that use significant resources are not available in truly low-resource languages . |
| Approach: | They propose improvements to entity candidate generation and disambiguation to make better use of the limited resources available in low-resource languages. |
| Outcome: | The proposed model gains 6-20% end-to-end linking accuracy on four low-resource languages. |
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Design Challenges in Low-resource Cross-lingual Entity Linking (2020.emnlp-main)
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| Challenge: | Existing techniques for grounding mentions of entities in a foreign language do not rise to the challenges introduced by text in low-resource languages (LRL) and fail to generalize to text not taken from Wikipedia, on which they are usually trained. |
| Approach: | They propose a cross-lingual XEL technique that uses search engines to locate and search for foreign language entries in Wikipedia. |
| Outcome: | The proposed system shows an increase of 25% in gold candidate recall and 13% in end-to-end linking accuracy over state-of-the-art baselines. |
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (2020.tacl-1)
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| Challenge: | Existing approaches to cross-lingual entity linking (XEL) do not extend well to low-resource languages with few Wikipedia pages. |
| Approach: | They propose to improve the model by combining Wikipedia references with a list of plausible candidate entities. |
| Outcome: | The proposed method yields 16.9% in Top-30 gold candidate recall compared with state-of-the-art models. |
Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)
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| Challenge: | Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision . |
| Approach: | They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly. |
| Outcome: | The proposed approach significantly improves on the current state-of-the-art in 8 languages. |
Efficient Entity Candidate Generation for Low-Resource Languages (2022.lrec-1)
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| Challenge: | Existing approaches for cross-lingual entity linking are not suitable for English. |
| Approach: | They propose a candidate generation problem in cross-lingual entity linking with a focus on low-resource languages. |
| Outcome: | The proposed solution outperforms the state-of-the-art approach on 9 real-world datasets and query types. |
Entity Linking in 100 Languages (2020.emnlp-main)
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| Challenge: | Existing approaches to multilingual entity linking are cross-lingual, with a focus on zero-shot evaluation. |
| Approach: | They propose a new formulation for multilingual entity linking where language-specific mentions resolve to a language-agnostic Knowledge Base. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large multilingual dataset and shows that frequency-based analysis provided key insights for the model and training enhancements. |
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages (D19-61)
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| Challenge: | a novel name retrieval method is proposed for languages with no annotations or training data. |
| Approach: | They propose a method which relies on zero annotation or resources from the target language . they pre-train an orthographic encoder using Wikipedia inter-lingual links from dozens of languages . |
| Outcome: | The proposed method shows 11.6% improvement over state-of-the-art methods. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
| Outcome: | The proposed model performs well in both monolingual and multilingual settings. |
Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining (D19-61)
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| Challenge: | Entities can be used as effective signals to generate less ambiguous semantic representations and align multiple languages. |
| Approach: | They propose a method to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. |
| Outcome: | The proposed method can generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia . it provides reliable alignment on word/entity level and sentence level, and thus can be used for unsupervised cross-linguistic entity linking. |
Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks (2021.naacl-main)
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| Challenge: | Masked language models have become the de facto standard when processing text . however, these models are evaluated in a monolingual setting only . |
| Approach: | They propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap between different languages. |
| Outcome: | The proposed approach bridges the gap between word representations and knowledge graphs by using a shared vocabulary of entities. |