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Revisiting Sparse Retrieval for Few-shot Entity Linking (2310.12444v1)

Published 19 Oct 2023 in cs.CL

Abstract: Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base. One of the key challenges comes from insufficient labeled data for specific domains. Although dense retrievers have achieved excellent performance on several benchmarks, their performance decreases significantly when only a limited amount of in-domain labeled data is available. In such few-shot setting, we revisit the sparse retrieval method, and propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. For training the extractor, we propose a distant supervision method to automatically generate training data based on overlapping tokens between mention contexts and entity descriptions. Experimental results on the ZESHEL dataset demonstrate that the proposed method outperforms state-of-the-art models by a significant margin across all test domains, showing the effectiveness of keyword-enhanced sparse retrieval.

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References (19)
  1. Autoregressive entity retrieval. In International Conference on Learning Representations.
  2. Electra: Pre-training text encoders as discriminators rather than generators. In International Conference on Learning Representations.
  3. Faithful to the document or to the world? mitigating hallucinations via entity-linked knowledge in abstractive summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1067–1082, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  4. Splade: Sparse lexical and expansion model for first stage ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 2288–2292, New York, NY, USA. Association for Computing Machinery.
  5. Heng Ji and Joel Nothman. 2016. Overview of TAC-KBP2016 tri-lingual EDL and its impact on end-to-end KBP. In Proceedings of the 2016 Text Analysis Conference, TAC 2016, Gaithersburg, Maryland, USA, November 14-15, 2016. NIST.
  6. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.
  7. Phong Le and Ivan Titov. 2019. Distant learning for entity linking with automatic noise detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4081–4090, Florence, Italy. Association for Computational Linguistics.
  8. You don’t know my favorite color: Preventing dialogue representations from revealing speakers’ private personas. arXiv preprint arXiv:2205.10228.
  9. Effective few-shot named entity linking by meta-learning. In 2022 IEEE 38th International Conference on Data Engineering (ICDE), pages 178–191, Los Alamitos, CA, USA. IEEE Computer Society.
  10. Zero-shot entity linking by reading entity descriptions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3449–3460, Florence, Italy. Association for Computational Linguistics.
  11. MuVER: Improving first-stage entity retrieval with multi-view entity representations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2617–2624, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  12. Curriculum contrastive context denoising for few-shot conversational dense retrieval. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’22, page 176–186, New York, NY, USA. Association for Computing Machinery.
  13. A thorough examination on zero-shot dense retrieval. CoRR, abs/2204.12755.
  14. Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr., 3(4):333–389.
  15. A transformational biencoder with in-domain negative sampling for zero-shot entity linking. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1449–1458, Dublin, Ireland. Association for Computational Linguistics.
  16. Cat: A contextualized conceptualization and instantiation framework for commonsense reasoning. arXiv preprint arXiv:2305.04808.
  17. Scalable zero-shot entity linking with dense entity retrieval. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6397–6407, Online. Association for Computational Linguistics.
  18. Prompting ELECTRA: Few-shot learning with discriminative pre-trained models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11351–11361, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  19. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, page 1253–1256, New York, NY, USA. Association for Computing Machinery.
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Authors (4)
  1. Yulin Chen (134 papers)
  2. Zhenran Xu (12 papers)
  3. Baotian Hu (67 papers)
  4. Min Zhang (630 papers)
Citations (1)
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