{"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/instructabsa-instruction-learning-for-aspect","title":"InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis","arxiv_id":"2302.08624","date":"2023-02-16","proceeding":null,"authors":["Kevin Scaria","Himanshu Gupta","Siddharth Goyal","Saurabh Arjun Sawant","Swaroop Mishra","Chitta Baral"],"abstract":"We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for ABSA subtasks, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentiment Pair Extraction (ASPE) subtasks. In particular, InstructABSA outperforms the previous state-of-the-art (SOTA) on the Rest14 ATE subtask by 5.69% points, the Rest15 ATSC subtask by 9.59% points, and the Lapt14 AOPE subtask by 3.37% points, surpassing 7x larger models. We also get competitive results on AOOE, AOPE, and AOSTE subtasks indicating strong generalization ability to all subtasks. Exploring sample efficiency reveals that just 50% train data is required to get competitive results with other instruction tuning approaches. Lastly, we assess the quality of instructions and observe that InstructABSA's performance experiences a decline of ~10% when adding misleading examples.","url_abs":"https://arxiv.org/abs/2302.08624v6","url_pdf":"https://arxiv.org/pdf/2302.08624v6.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":"instructabsa-instruction-learning-for-aspect","repo_url":"https://github.com/kevinscaria/instructabsa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"aspect-extraction","task_name":"Aspect 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":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"term-extraction","task_name":"Term Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-extraction-on-semeval-2014-task-4-sub-2","task":"Aspect Extraction","dataset":"SemEval 2014 Task 4 Sub Task 1","model":"InstructABSA","rank_in_archive_order":1,"of":2,"metrics":{"Laptop (F1)":"92.30"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-extraction-on-semeval-2014-task-4-sub-1","task":"Aspect Extraction","dataset":"SemEval-2014 Task-4","model":"InstructABSA","rank_in_archive_order":1,"of":6,"metrics":{"Laptop (F1)":"92.30","Mean F1 (Laptop + Restaurant)":"92.53","Restaurant (F1)":"92.76"},"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":"InstructABSA","rank_in_archive_order":1,"of":9,"metrics":{"F1":"79.34"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-7","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Sub Task 1","model":"InstructABSA","rank_in_archive_order":1,"of":4,"metrics":{"Laptop (F1)":"92.30","Restaurant (F1)":"92.76"},"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":"InstructABSA","rank_in_archive_order":2,"of":10,"metrics":{"F1":"79.34"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval-2014 Task-4","model":"InstructABSA","rank_in_archive_order":18,"of":48,"metrics":{"Laptop (Acc)":"80.56","Mean Acc (Restaurant + Laptop)":"81.5","Restaurant (Acc)":"82.44"},"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":"InstructABSA","rank_in_archive_order":1,"of":8,"metrics":{"F1":"79.34"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.08624","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}