{"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/diaasq-a-benchmark-of-conversational-aspect","title":"DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis","arxiv_id":"2211.05705","date":"2022-11-10","proceeding":null,"authors":["Bobo Li","Hao Fei","Fei Li","Yuhan Wu","Jinsong Zhang","Shengqiong Wu","Jingye Li","Yijiang Liu","Lizi Liao","Tat-Seng Chua","Donghong Ji"],"abstract":"The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.","url_abs":"https://arxiv.org/abs/2211.05705v4","url_pdf":"https://arxiv.org/pdf/2211.05705v4.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":"diaasq-a-benchmark-of-conversational-aspect","repo_url":"https://github.com/unikcc/diaasq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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":"conversational-sentiment-quadruple-extraction","task_name":"Conversational Sentiment Quadruple Extraction"},{"task_slug":"opinion-mining","task_name":"Opinion Mining"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[{"slug":"diaasq","name":"DiaASQ","full_name":"Conversational Aspect-based Sentiment Quadruple Extraction"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/conversational-sentiment-quadruple-extraction","task":"Conversational Sentiment Quadruple Extraction","dataset":"DiaASQ (EN)","model":"E2E-DiaASQ","rank_in_archive_order":1,"of":1,"metrics":{"Pair F1 (aspect-opinion)":"44.27","Pair F1 (target-aspect)":"47.91","Pair F1 (target-opinion)":"45.58","Quad F1 (identification)":"36.80","Quad F1 (micro)":"33.31","Span F1 (aspect)":"74.71","Span F1 (opinion)":"60.22","Span F1 (target)":"88.62"},"uses_additional_data":false},{"leaderboard":"/sota/conversational-sentiment-quadruple-extraction-1","task":"Conversational Sentiment Quadruple Extraction","dataset":"DiaASQ (ZH)","model":"E2E-DiaASQ","rank_in_archive_order":1,"of":1,"metrics":{"Pair F1 (aspect-opinion)":"45.44","Pair F1 (target-aspect)":"48.61","Pair F1 (target-opinion)":"43.31","Quad F1 (identification)":"37.51","Quad F1 (micro)":"34.94","Span F1 (aspect)":"76.94","Span F1 (opinion)":"59.35","Span F1 (target)":"90.23"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.05705","atlas_url":"https://app.syntology.ai/?focus=2211.05705","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}