{"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/relation-aware-collaborative-learning-for","title":"Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis","arxiv_id":null,"date":"2020-07-01","proceeding":"ACL 2020 6","authors":["Zhuang Chen","Tieyun Qian"],"abstract":"Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is \\textit{{``}delicious{''}}, the aspect term must be \\textit{{``}food{''}} rather than \\textit{{``}place{''}}. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.","url_abs":"https://aclanthology.org/2020.acl-main.340","url_pdf":"https://aclanthology.org/2020.acl-main.340.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":"relation-aware-collaborative-learning-for","repo_url":"https://github.com/NLPWM-WHU/RACL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"aspect-term-extraction-and-sentiment","task_name":"Aspect Term Extraction and Sentiment Classification"},{"task_slug":"aspect-based-sentiment-analysis-1","task_name":"Aspect-Based Sentiment Analysis"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"term-extraction","task_name":"Term Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-term-extraction-and-sentiment","task":"Aspect Term Extraction and Sentiment Classification","dataset":"SemEval","model":"RACL-BERT","rank_in_archive_order":4,"of":6,"metrics":{"Avg F1":"68.29","Laptop 2014 (F1)":"63.4","Restaurant 2014 (F1)":"75.42","Restaurant 2015 (F1)":"66.05"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-5","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Laptop","model":"RACL-BERT","rank_in_archive_order":4,"of":9,"metrics":{"F1":"63.4"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-6","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Subtask 1+2","model":"RACL-BERT","rank_in_archive_order":6,"of":10,"metrics":{"F1":"63.4"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-semeval-2014-task-4","task":"Sentiment Analysis","dataset":"SemEval 2014 Task 4 Subtask 1+2","model":"RACL-BERT","rank_in_archive_order":4,"of":8,"metrics":{"F1":"63.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}