| Challenge: | Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately. |
| Approach: | They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder . |
| Outcome: | The proposed system can select the best expansion from a significant amount of potential events quickly and accurately. |
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| Challenge: | LLM-based methods often generate narrowly focused expansions that overlook these desiderata. |
| Approach: | They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions . |
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Event-enhanced Retrieval in Real-time Search (2024.lrec-main)
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DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)
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| Challenge: | Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model . |
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Towards Better Question Generation in QA-based Event Extraction (2024.findings-acl)
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| Challenge: | True. True. EE aims to extract event-related information from unstructured texts. |
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BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
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| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
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Corpus-Steered Query Expansion with Large Language Models (2024.eacl-short)
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| Challenge: | Recent studies show query expansions generate hypothetical documents that answer queries as expansions. |
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Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (2022.acl-short)
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| Challenge: | Existing approaches to extract value from product data for a large number of attributes are not effective for rare and ambiguous attributes. |
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Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
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Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA (2024.findings-acl)
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| Challenge: | Existing methods like GAR and EAR rely heavily on supervised training and struggle to maintain effectiveness across domains and datasets. |
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Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
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