Papers › Dialogue-Based Relation Extraction
Dialogue-Based Relation Extraction
Dian Yu, Kai Sun, Claire Cardie, Dong Yu
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.
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Tasks
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Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dialog Relation Extraction | DialogRE | BERTS | F1 (v1) | 61.2 | #15 of 17 | Archive leaderboard | report |
| Dialog Relation Extraction | DialogRE | BERTS | F1c (v1) | 55.4 | #15 of 17 | Archive leaderboard | report |
| Dialog Relation Extraction | DialogRE | BiLSTM | F1 (v1) | 48.6 | #17 of 17 | Archive leaderboard | report |
| Dialog Relation Extraction | DialogRE | BiLSTM | F1c (v1) | 45 | #17 of 17 | 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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