{"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-robustly-optimized-bmrc-for-aspect","title":"A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL 2022 7","authors":["Shu Liu","Kaiwen Li","Zuhe Li"],"abstract":"Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but several problems remains, such as query conflict and probability unilateral decrease. Therefore, this paper presents a robustly optimized BMRC method by incorporating four improvements. The word segmentation is applied to facilitate the semantic learning. Exclusive classifiers are designed to avoid the interference between different queries. A span matching rule is proposed to select the aspects and opinions that better represent the expectations of the model. The probability generation strategy is also introduced to obtain the predicted probability for aspects, opinions and aspect-opinion pairs. We have conducted extensive experiments on multiple benchmark datasets, where our model achieves the state-of-the-art performance.","url_abs":"https://aclanthology.org/2022.naacl-main.20","url_pdf":"https://aclanthology.org/2022.naacl-main.20.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":"a-robustly-optimized-bmrc-for-aspect","repo_url":"https://github.com/itkaven/robmrc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"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":"machine-reading-comprehension","task_name":"Machine Reading Comprehension"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}