Papers › Neural Metric Learning for Fast End-to-End Relation Extraction
Neural Metric Learning for Fast End-to-End Relation Extraction
Tung Tran, Ramakanth Kavuluru
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | Adverse Drug Events (ADE) Corpus | Relation-Metric | NER Macro F1 | 87.02 | #13 of 15 | Archive leaderboard | report |
| Relation Extraction | Adverse Drug Events (ADE) Corpus | Relation-Metric | RE+ Macro F1 | 77.19 | #13 of 15 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | Relation-Metric with AT | NER Macro F1 | 84.15 | #6 of 16 | Archive leaderboard | report |
| Relation Extraction | CoNLL04 | Relation-Metric with AT | RE+ Macro F1 | 62.29 | #6 of 16 | Archive leaderboard | report |
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