The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Deep entity matching with pre-trained language models
Open accessAbstract
We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a straight-forward application of language models such as BERT, DistilBERT, or RoBERTa pre-trained on large text corpora already significantly improves the matching quality and outperforms previous state-of-the-art (SOTA), by up to 29% of F1 score on benchmark datasets. We also developed three optimization techniques to further improve Ditto's matching capability. Ditto allows domain knowledge to be injected by highlighting important pieces of input information that may be of interest when making matching decisions. Ditto also summarizes strings that are too long so that only the essential information is retained and used for EM. Finally, Ditto adapts a SOTA technique on data augmentation for text to EM to augment the training data with (difficult) examples. This way, Ditto is forced to learn "harder" to improve the model's matching capability. The optimizations we developed further boost the performance of Ditto by up to 9.8%. Perhaps more surprisingly, we establish that Ditto can achieve the previous SOTA results with at most half the number of labeled data. Finally, we demonstrate Ditto's effectiveness on a real-world large-scale EM task. On matching two company datasets consisting of 789K and 412K records, Ditto achieves a high F1 score of 96.5%.
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Cited by
Transformers for Tabular Data Representation: A Survey of Models and Applications
Badaro, G., Saeed, M., & Papotti, P. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00544 · Cited by 96
A Large-scale Offer Alignment Model for Partitioning Filtering and Matching Product Offers
Huang, W., Melo, A., & Pan, J. Z. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3661351
Metadata
- Authors
- Li, Y., Li, J., Suhara, Y., Doan, A., & Tan, W.-C.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2020-09
- Volume
- 14
- Issue
- 1
- Pages
- 50-60
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W3014705052
- Cited by
- 339
- References
- 40
- FWCI
- 20.56
- Type
- journal article
- Language
- en
- Open access
- green
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