{"url":"/dataset/aste","name":"ASTE","full_name":"Aspect Sentiment Triplet Extraction","description_markdown":"Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.","description_withheld":null,"homepage":"","introduced_date":"2019-11-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/knowing-what-how-and-why-a-near-complete","title":"Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis","first_author":"Haiyun Peng","url":null},"license":null,"modalities":[],"tasks":[{"name":"Aspect-Based Sentiment Analysis (ABSA)","url":"/task/aspect-based-sentiment-analysis","datasets_with_task":"/datasets/task/aspect-based-sentiment-analysis"}],"languages":[],"variants":["ASTE"],"data_loaders":[],"num_papers_in_archive":64,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-aste","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset_variant":"ASTE","rows":13,"metrics":["F1 (L14)","F1(R14)","F1 (R15)","F1 (R16)"],"first_row_in_archive_order":{"model":"MvP (multi-task)","paper":"/paper/mvp-multi-view-prompting-improves-aspect","metrics":{"F1 (L14)":"65.30","F1 (R15)":"69.44","F1 (R16)":"73.10","F1(R14)":"76.30"},"code_links":[{"title":"ZubinGou/multi-view-prompting","url":"https://github.com/ZubinGou/multi-view-prompting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/aste-transformer-modelling-dependencies-in","title":"ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction","date":"2024-09-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/generative-data-augmentation-for-aspect","title":"Generative Data Augmentation for Aspect Sentiment Quad Prediction","date":"2023-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mvp-multi-view-prompting-improves-aspect","title":"MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction","date":"2023-05-22","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/improving-aspect-sentiment-quad-prediction","title":"Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation","date":"2022-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lego-absa-a-prompt-based-task-assemblable","title":"LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis","date":"2022-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/seq2path-generating-sentiment-tuples-as-paths","title":"Seq2Path: Generating Sentiment Tuples as Paths of a Tree","date":"2022-05-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unified-structure-generation-for-universal","title":"Unified Structure Generation for Universal Information Extraction","date":"2022-03-23","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/aspect-sentiment-quad-prediction-as","title":"Aspect Sentiment Quad Prediction as Paraphrase Generation","date":"2021-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-generative-aspect-based-sentiment","title":"Towards Generative Aspect-Based Sentiment Analysis","date":"2021-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-span-level-interactions-for-aspect","title":"Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction","date":"2021-07-26","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}