# Neural Metric Learning for Fast End-to-End Relation Extraction

arXiv: https://arxiv.org/abs/1905.07458 · HTML: https://mlsanity.com/paper/1905.07458/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 12 of 12)

- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference](https://mlsanity.com/paper/2105.13456/) (2021)
- [Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition](https://mlsanity.com/paper/2208.03054/) (2022)
- [Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning](https://mlsanity.com/paper/2109.00840/) (2021)
- [FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework](https://mlsanity.com/paper/2205.02490/) (2022)
- [End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies](https://mlsanity.com/paper/2304.01344/) (2023)
- [End-to-End n-ary Relation Extraction for Combination Drug Therapies](https://mlsanity.com/paper/2303.16886/) (2023)
- [Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network](https://mlsanity.com/paper/2202.07281/) (2022)
- [Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case](https://mlsanity.com/paper/2311.13729/) (2023)
- [A Benchmark for End-to-End Zero-Shot Biomedical Relation Extraction with LLMs: Experiments with OpenAI Models](https://mlsanity.com/paper/2504.04083/) (2025)
- [AutoIE: An Automated Framework for Information Extraction from Scientific Literature](https://mlsanity.com/paper/2401.16672/) (2024)
- [Generalized knowledge-enhanced framework for biomedical entity and relation extraction](https://mlsanity.com/paper/2408.06618/) (2024)

## Abstract (arXiv)

Relation extraction (RE) is an indispensable information extraction task in several disciplines. RE models typically assume that named entity recognition (NER) is already performed in a previous step by another independent model. Several recent efforts, under the theme of end-to-end RE, seek to exploit inter-task correlations by modeling both NER and RE tasks jointly. Earlier work in this area commonly reduces the task to a table-filling problem wherein an additional expensive decoding step involving beam search is applied to obtain globally consistent cell labels. In efforts that do not employ table-filling, global optimization in the form of CRFs with Viterbi decoding for the NER component is still necessary for competitive performance. We introduce a novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, that improves on the state-of-the-art without the need for global optimization. We validate our model on the ADE and CoNLL04 datasets for end-to-end RE and demonstrate ≈ 1% gain (in F-score) over prior best results with training and testing times that are seven to ten times faster --- the latter highly advantageous for time-sensitive end user applications.