{"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/attentional-encoder-network-for-targeted","title":"Attentional Encoder Network for Targeted Sentiment Classification","arxiv_id":"1902.09314","date":"2019-02-25","proceeding":null,"authors":["Youwei Song","Jiahai Wang","Tao Jiang","Zhiyue Liu","Yanghui Rao"],"abstract":"Targeted sentiment classification aims at determining the sentimental\ntendency towards specific targets. Most of the previous approaches model\ncontext and target words with RNN and attention. However, RNNs are difficult to\nparallelize and truncated backpropagation through time brings difficulty in\nremembering long-term patterns. To address this issue, this paper proposes an\nAttentional Encoder Network (AEN) which eschews recurrence and employs\nattention based encoders for the modeling between context and target. We raise\nthe label unreliability issue and introduce label smoothing regularization. We\nalso apply pre-trained BERT to this task and obtain new state-of-the-art\nresults. Experiments and analysis demonstrate the effectiveness and lightweight\nof our model.","url_abs":"http://arxiv.org/abs/1902.09314v2","url_pdf":"http://arxiv.org/pdf/1902.09314v2.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":"attentional-encoder-network-for-targeted","repo_url":"https://github.com/songyouwei/ABSA-PyTorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"attentional-encoder-network-for-targeted","repo_url":"https://github.com/Ankur3107/awesome-daily-blog","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"attentional-encoder-network-for-targeted","repo_url":"https://github.com/mindspore-courses/ABSA-MindSpore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}},{"paper_slug":"attentional-encoder-network-for-targeted","repo_url":"https://github.com/recommeddit/labs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"attentional-encoder-network-for-targeted","repo_url":"https://github.com/yangheng95/LC-ABSA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval-2014 Task-4","model":"BERT-SPC","rank_in_archive_order":15,"of":48,"metrics":{"Laptop (Acc)":"78.99","Mean Acc (Restaurant + Laptop)":"81.73","Restaurant (Acc)":"84.46"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval-2014 Task-4","model":"AEN-BERT","rank_in_archive_order":16,"of":48,"metrics":{"Laptop (Acc)":"79.93","Mean Acc (Restaurant + Laptop)":"81.53","Restaurant (Acc)":"83.12"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval-2014 Task-4","model":"AEN-GloVe","rank_in_archive_order":33,"of":48,"metrics":{"Laptop (Acc)":"73.51","Mean Acc (Restaurant + Laptop)":"77.25","Restaurant (Acc)":"80.98"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-twitter","task":"Sentiment Analysis","dataset":"Twitter","model":"AEN-BERT","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"74.71"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-twitter","task":"Sentiment Analysis","dataset":"Twitter","model":"BERT-SPC","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"73.55"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-twitter","task":"Sentiment Analysis","dataset":"Twitter","model":"AEN-GloVe","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"72.83"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09314","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}