# Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

arXiv: https://arxiv.org/abs/2106.04174 · HTML: https://mlsanity.com/paper/2106.04174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Low-resource Deep Entity Resolution with Transfer and Active Learning](https://mlsanity.com/paper/1906.08042/) (2019)
- [Modeling Voting for System Combination in Machine Translation](https://mlsanity.com/paper/2007.06943/) (2020)
- [PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation](https://mlsanity.com/paper/1906.11455/) (2019)
- [Reuse and Adaptation for Entity Resolution through Transfer Learning](https://mlsanity.com/paper/1809.11084/) (2018)
- [Active Deep Learning on Entity Resolution by Risk Sampling](https://mlsanity.com/paper/2012.12960/) (2020)
- [EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs](https://mlsanity.com/paper/2101.06126/) (2021)
- [Towards Interpretable and Learnable Risk Analysis for Entity Resolution](https://mlsanity.com/paper/1912.02947/) (2019)

## Built on by (most cited first, 2 of 2)

- [Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2304.12329/) (2023)
- [A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms](https://mlsanity.com/paper/2307.01231/) (2023)

## Abstract (arXiv)

Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. Our codes and datasets can be obtained from https://github.com/THU-KEG/HIF-KAT.