{"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/rethinking-the-evaluation-of-pre-trained-text","title":"Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective","arxiv_id":"2402.02379","date":"2024-02-04","proceeding":null,"authors":["Chong Zhang","Yixi Zhao","Chenshu Yuan","Yi Tu","Ya Guo","Qi Zhang"],"abstract":"Recently developed pre-trained text-and-layout models (PTLMs) have shown remarkable success in multiple information extraction tasks on visually-rich documents. However, the prevailing evaluation pipeline may not be sufficiently robust for assessing the information extraction ability of PTLMs, due to inadequate annotations within the benchmarks. Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs. We then introduce EC-FUNSD, an entity-centric benckmark designed for the evaluation of semantic entity recognition and entity linking on visually-rich documents. This dataset contains diverse formats of document layouts and annotations of semantic-driven entities and their relations. Moreover, this dataset disentangles the falsely coupled annotation of segment and entity that arises from the block-level annotation of FUNSD. Experiment results demonstrate that state-of-the-art PTLMs exhibit overfitting tendencies on the prevailing benchmarks, as their performance sharply decrease when the dataset bias is removed.","url_abs":"https://arxiv.org/abs/2402.02379v1","url_pdf":"https://arxiv.org/pdf/2402.02379v1.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":"rethinking-the-evaluation-of-pre-trained-text","repo_url":"https://github.com/chongzhangFDU/ROOR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"semantic-entity-labeling","task_name":"Semantic entity labeling"}],"methods":[],"datasets_introduced":[{"slug":"ec-funsd","name":"EC-FUNSD","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-linking-on-ec-funsd","task":"Entity Linking","dataset":"EC-FUNSD","model":"GeoLayoutLM","rank_in_archive_order":2,"of":8,"metrics":{"F1":"86.18"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-ec-funsd","task":"Entity Linking","dataset":"EC-FUNSD","model":"LayoutLMv3 (large)","rank_in_archive_order":4,"of":8,"metrics":{"F1":"78.14"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-ec-funsd","task":"Entity Linking","dataset":"EC-FUNSD","model":"LayoutLMv3 (base)","rank_in_archive_order":7,"of":8,"metrics":{"F1":"67.47"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-entity-labeling-on-ec-funsd","task":"Semantic entity labeling","dataset":"EC-FUNSD","model":"LayoutLMv3 (large)","rank_in_archive_order":3,"of":8,"metrics":{"F1":"83.88"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-entity-labeling-on-ec-funsd","task":"Semantic entity labeling","dataset":"EC-FUNSD","model":"GeoLayoutLM","rank_in_archive_order":5,"of":8,"metrics":{"F1":"83.62"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-entity-labeling-on-ec-funsd","task":"Semantic entity labeling","dataset":"EC-FUNSD","model":"LayoutLMv3 (base)","rank_in_archive_order":7,"of":8,"metrics":{"F1":"82.30"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.02379","atlas_url":"https://app.syntology.ai/?focus=2402.02379","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}