Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914988032000000 |
|---|---|
| author | Hwang, Dae Yon Taha, Bilal Pande, Harshit Nechaev, Yaroslav |
| author_facet | Hwang, Dae Yon Taha, Bilal Pande, Harshit Nechaev, Yaroslav |
| contents | Despite the recent advancements in information retrieval (IR), zero-shot IR remains a significant challenge, especially when dealing with new domains, languages, and newly-released use cases that lack historical query traffic from existing users. For such cases, it is common to use query augmentations followed by fine-tuning pre-trained models on the document data paired with synthetic queries. In this work, we propose a novel Universal Document Linking (UDL) algorithm, which links similar documents to enhance synthetic query generation across multiple datasets with different characteristics. UDL leverages entropy for the choice of similarity models and named entity recognition (NER) for the link decision of documents using similarity scores. Our empirical studies demonstrate the effectiveness and universality of the UDL across diverse datasets and IR models, surpassing state-of-the-art methods in zero-shot cases. The developed code for reproducibility is included in https://github.com/eoduself/UDL |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval Hwang, Dae Yon Taha, Bilal Pande, Harshit Nechaev, Yaroslav Artificial Intelligence Information Retrieval Machine Learning Despite the recent advancements in information retrieval (IR), zero-shot IR remains a significant challenge, especially when dealing with new domains, languages, and newly-released use cases that lack historical query traffic from existing users. For such cases, it is common to use query augmentations followed by fine-tuning pre-trained models on the document data paired with synthetic queries. In this work, we propose a novel Universal Document Linking (UDL) algorithm, which links similar documents to enhance synthetic query generation across multiple datasets with different characteristics. UDL leverages entropy for the choice of similarity models and named entity recognition (NER) for the link decision of documents using similarity scores. Our empirical studies demonstrate the effectiveness and universality of the UDL across diverse datasets and IR models, surpassing state-of-the-art methods in zero-shot cases. The developed code for reproducibility is included in https://github.com/eoduself/UDL |
| title | Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval |
| topic | Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2410.18385 |