{"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-joint-training-dual-mrc-framework-for","title":"A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis","arxiv_id":"2101.00816","date":"2021-01-04","proceeding":null,"authors":["Yue Mao","Yi Shen","Chao Yu","Longjun Cai"],"abstract":"Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a complete solution for ABSA. We construct two machine reading comprehension (MRC) problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing. We conduct experiments on these subtasks, and results on several benchmark datasets demonstrate the effectiveness of our proposed framework, which significantly outperforms existing state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2101.00816v2","url_pdf":"https://arxiv.org/pdf/2101.00816v2.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":[],"tasks":[{"task_slug":"aspect-sentiment-triplet-extraction","task_name":"Aspect Sentiment Triplet Extraction"},{"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":"aspect-oriented-opinion-extraction","task_name":"Aspect-oriented  Opinion Extraction"},{"task_slug":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"sentence","task_name":"Sentence"},{"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-sentiment-triplet-extraction-on","task":"Aspect Sentiment Triplet Extraction","dataset":"SemEval","model":"Dual-MRC","rank_in_archive_order":2,"of":4,"metrics":{"F1":"70.32"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-term-extraction-and-sentiment","task":"Aspect Term Extraction and Sentiment Classification","dataset":"SemEval","model":"Dual-MRC","rank_in_archive_order":3,"of":6,"metrics":{"Avg F1":"68.99","Laptop 2014 (F1)":"65.94","Restaurant 2014 (F1)":"75.95","Restaurant 2015 (F1)":"65.08"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-oriented-opinion-extraction-on-semeval","task":"Aspect-oriented  Opinion Extraction","dataset":"SemEval-2014 Task-4","model":"Dual-MRC","rank_in_archive_order":2,"of":5,"metrics":{"Laptop 2014 (F1)":"79.90","Restaurant 2014 (F1)":"83.73","Restaurant 2015 (F1)":"74.50","Restaurant 2016 (F1)":"83.33"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.00816","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}