Papers › Sequence Generation with Label Augmentation for Relation Extraction

Sequence Generation with Label Augmentation for Relation Extraction

29 Dec 2022arXiv:2212.14266archive 2025-07-28

Bo Li, Dingyao Yu, Wei Ye, Jinglei Zhang, Shikun Zhang

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

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Code

pkuserc/rela officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Relation Extraction

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Relation Extraction Google RE RELA F1 93.9 #1 of 1 Archive leaderboard report
Relation Extraction SemEval-2010 Task-8 RELA F1 90.4 #6 of 31 Archive leaderboard report
Relation Extraction TACRED RELA F1 71.2 #20 of 40 Archive leaderboard report
Relation Extraction sciERC-sent RELA F1 90.3 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

LSTMSeq2SeqSigmoid ActivationTanh Activation

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