{"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/nonautoregressive-encoder-decoder-neural","title":"Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction","arxiv_id":null,"date":"2021-12-03","proceeding":"IEEE 2021 12","authors":["Hao Fei","Yafeng Ren","Yue Zhang","Donghong Ji"],"abstract":"Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.","url_abs":"https://ieeexplore.ieee.org/document/9634849/authors#authors","url_pdf":"https://ieeexplore.ieee.org/document/9634849/authors#authors","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":[],"tasks":[{"task_slug":"aspect-sentiment-triplet-extraction","task_name":"Aspect Sentiment Triplet Extraction"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-sentiment-triplet-extraction-on-aste","task":"Aspect Sentiment Triplet Extraction","dataset":"ASTE-Data-V2","model":"NAT","rank_in_archive_order":4,"of":11,"metrics":{"F1":"72.84"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}