Papers › HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction

HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction

30 Jun 2021Findings (ACL) 2021 8arXiv:2106.15838archive 2025-07-28

Liliang Ren, Chenkai Sun, Heng Ji, Julia Hockenmaier

Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts. Existing approaches, such as table filling and pairwise scoring, have shown impressive performance on various information extraction tasks, but they are difficult to scale to datasets with longer input texts because of their second-order space/time complexities with respect to the input length. In this work, we propose a Hybrid Span Generator (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. Extensive experiments on the ACE05 dataset show that our approach also significantly outperforms state-of-the-art on the joint entity and relation extraction task.

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Tasks

DecoderJoint Entity and Relation ExtractionRelation Extraction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Relation Extraction ACE 2005 HySPA (ours) w/ RoBERTa Relation F1 68.2 #29 of 30 Archive leaderboard report
Relation Extraction ACE 2005 HySPA Cross Sentence No #30 of 30 Archive leaderboard report
Relation Extraction ACE 2005 HySPA Sentence Encoder ALBERT #30 of 30 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.

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