{"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/towards-generative-aspect-based-sentiment","title":"Towards Generative Aspect-Based Sentiment Analysis","arxiv_id":null,"date":"2021-08-01","proceeding":"ACL 2021 5","authors":["Wenxuan Zhang","Xin Li","Yang Deng","Lidong Bing","Wai Lam"],"abstract":"Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional taskspecific model design.","url_abs":"https://aclanthology.org/2021.acl-short.64/","url_pdf":"https://aclanthology.org/2021.acl-short.64.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":"towards-generative-aspect-based-sentiment","repo_url":"https://github.com/IsakZhang/Generative-ABSA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-sentiment-triplet-extraction","task_name":"Aspect Sentiment Triplet Extraction"},{"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":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-sentiment-triplet-extraction-on-aste","task":"Aspect Sentiment Triplet Extraction","dataset":"ASTE-Data-V2","model":"GAS","rank_in_archive_order":5,"of":11,"metrics":{"F1":"72.16"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-sentiment-triplet-extraction-on-1","task":"Aspect Sentiment Triplet Extraction","dataset":"MuseASTE","model":"GAS","rank_in_archive_order":4,"of":4,"metrics":{"F1":"0.218"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-asqp","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"ASQP","model":"GAS","rank_in_archive_order":7,"of":12,"metrics":{"F1 (R15)":"45.98","F1 (R16)":"56.03"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-aste","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"ASTE","model":"GAS","rank_in_archive_order":11,"of":13,"metrics":{"F1 (L14)":"58.19","F1 (R15)":"60.23","F1 (R16)":"69.05","F1(R14)":"70.52"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-tasd","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"TASD","model":"GAS","rank_in_archive_order":7,"of":11,"metrics":{"F1 (R15)":"60.63","F1 (R16)":"68.31"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}