{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-sequence-to-sequence-approach-for-document","title":"A sequence-to-sequence approach for document-level relation extraction","arxiv_id":"2204.01098","date":"2022-04-03","proceeding":"BioNLP (ACL) 2022 5","authors":["John Giorgi","Gary D. Bader","Bo wang"],"abstract":"Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach. Our code, data and trained models are available at {\\url{https://github.com/johngiorgi/seq2rel}}. An online demo is available at {\\url{https://share.streamlit.io/johngiorgi/seq2rel/main/demo.py}}.","url_abs":"https://arxiv.org/abs/2204.01098v2","url_pdf":"https://arxiv.org/pdf/2204.01098v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-sequence-to-sequence-approach-for-document","repo_url":"https://github.com/johngiorgi/seq2rel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-sequence-to-sequence-approach-for-document","repo_url":"https://github.com/JohnGiorgi/seq2rel-ds","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"coreference-resolution","task_name":"Coreference Resolution"},{"task_slug":"document-level-relation-extraction","task_name":"Document-level Relation Extraction"},{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"coreference-resolution-1","task_name":"coreference-resolution"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"seq2seq","method_name":"Seq2Seq"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-cdr","task":"Joint Entity and Relation Extraction","dataset":"CDR","model":"seq2rel","rank_in_archive_order":1,"of":1,"metrics":{"Relation F1":"40.2"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-3","task":"Joint Entity and Relation Extraction","dataset":"DocRED","model":"seq2rel","rank_in_archive_order":6,"of":6,"metrics":{"Relation F1":"38.2"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-gda","task":"Joint Entity and Relation Extraction","dataset":"GDA","model":"seq2rel","rank_in_archive_order":1,"of":1,"metrics":{"Relation F1":"55.2"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-cdr","task":"Relation Extraction","dataset":"CDR","model":"seq2rel (entity hinting)","rank_in_archive_order":8,"of":10,"metrics":{"F1":"67.2"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-gda","task":"Relation Extraction","dataset":"GDA","model":"seq2rel (entity hinting)","rank_in_archive_order":5,"of":9,"metrics":{"F1":"84.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.01098","atlas_url":"https://app.syntology.ai/?focus=2204.01098","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}