Cross-Language Learning for Entity Matching
Transformer-based entity matching methods have significantly moved the state of the art for less-structured matching tasks such as matching product offers in e-commerce. In order to excel at these tasks, Transformer-based matching methods require a decent amount of training pairs. Providing enough training data can be challenging, especially if a matcher for non-English product descriptions should be learned. This poster explores along the use case of matching product offers from different e-shops to which extent it is possible to improve the performance of Transformer-based matchers by complementing a small set of training pairs in the target language, German in our case, with a larger set of English-language training pairs. Our experiments using different Transformers show that extending the German set with English pairs improves the matching performance in all cases. The impact of adding the English pairs is especially high in low-resource settings in which only a rather small number of non-English pairs is available. As it is often possible to automatically gather English training pairs from the Web by exploiting schema.org annotations, our results are relevant for many product matching scenarios targeting low-resource languages.
Code (1)
Tasks
Cross-Lingual TransferEntity ResolutionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unlocking the Power of Large Language Models for Multi-table Entity Matching
Multi-table entity matching (MEM) addresses the limitations of dual-table approaches by enabling simultaneous identification of equivalent entities across multiple data sources without unique identifiers. However, existi…
Fine-tuning Large Language Models for Entity Matching
Generative large language models (LLMs) are a promising alternative to pre-trained language models for entity matching due to their high zero-shot performance and ability to generalize to unseen entities. Existing resear…
Data IntegrationEntity ResolutionIn-Context LearningPrompt EngineeringWDC Products: A Multi-Dimensional Entity Matching Benchmark
The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the si…
Contrastive LearningData IntegrationEntity ResolutionMulti-class ClassificationConfidence Calibration in Large Language Model-Based Entity Matching
This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baseline RoBERTa confidences for an Entity Matc…
Leveraging large language models for efficient representation learning for entity resolution
In this paper, the authors propose TriBERTa, a supervised entity resolution system that utilizes a pre-trained large language model and a triplet loss function to learn representations for entity matching. The system con…
BlockingContrastive LearningEntity ResolutionLanguage Modeling+5