{"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/generative-data-augmentation-for-aspect","title":"Generative Data Augmentation for Aspect Sentiment Quad Prediction","arxiv_id":null,"date":"2023-07-01","proceeding":"starsem 2023 7","authors":["An Wang","Junfeng Jiang","Youmi Ma","Ao Liu","Naoaki Okazaki"],"abstract":"Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text. One challenge in this task is the scarcity of data owing to the high annotation cost. Data augmentation techniques are commonly used to address this issue. However, existing approaches simply rewrite texts in the training data, restricting the semantic diversity of the generated data and impairing the quality due to the inconsistency between text and quads. To address these limitations, we augment quads and train a quads-to-text model to generate corresponding texts. Furthermore, we designed novel strategies to filter out low-quality data and balance the sample difficulty distribution of the augmented dataset. Empirical studies on two ASQP datasets demonstrate that our method outperforms other data augmentation methods and achieves state-of-the-art performance on the benchmarks.","url_abs":"https://aclanthology.org/2023.starsem-1.12/","url_pdf":"https://aclanthology.org/2023.starsem-1.12.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":"generative-data-augmentation-for-aspect","repo_url":"https://github.com/AnWang-AI/AugABSA","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-asqp","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"ASQP","model":"AugABSA","rank_in_archive_order":3,"of":12,"metrics":{"F1 (R15)":"50.01","F1 (R16)":"60.88"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-aste","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"ASTE","model":"AugABSA","rank_in_archive_order":6,"of":13,"metrics":{"F1 (L14)":"62.66","F1 (R15)":"65.80","F1 (R16)":"74.23","F1(R14)":"73.76"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}