{"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/cae-mechanism-to-diminish-the-class","title":"CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task","arxiv_id":null,"date":"2022-09-21","proceeding":"Advances in Computational Collective Intelligence 2022 9","authors":["Nguyen Minh Phuong","Tung Le","Nguyen Le Minh"],"abstract":"Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance. However, classed imbalance problem in NLU has not been carefully investigated, while this problem in Semantic Parsing datasets is frequent. Therefore, this work focuses on diminishing this problem. We proposed a BERT-based architecture named JointBERT Classify Anonymous Entity (JointBERT-CAE) that improves the performance of the system on three Semantic Parsing datasets ATIS, Snips, ATIS Vietnamese, and a well-known Named Entity Recognize (NER) dataset CoNLL2003. In JointBERT-CAE architecture, we use multitask joint-learning to split conventional Slot Filling task into two sub-task, detect Anonymous Entity by Sequence tagging and Classify recognized anonymous entities tasks. The experimental results show the solid improvement of JointBERT-CAE when compared with BERT on all datasets, as well as the wide applicable capacity to other NLP tasks using the Sequence Tagging technique.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-16210-7_12","url_pdf":"https://link.springer.com/content/pdf/10.1007/978-3-031-16210-7_12.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":"cae-mechanism-to-diminish-the-class","repo_url":"https://github.com/phuongnm94/JointBERT_CAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"intent-classification","task_name":"Intent Classification"},{"task_slug":"intent-classification-and-slot-filling","task_name":"Intent Classification and Slot Filling"},{"task_slug":"intent-detection","task_name":"Intent Detection"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"},{"task_slug":"intent-classification-1","task_name":"intent-classification"},{"task_slug":"slot-filling-1","task_name":"slot-filling"}],"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":"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/intent-detection-on-atis","task":"Intent Detection","dataset":"ATIS","model":"JointBERT-CAE","rank_in_archive_order":11,"of":16,"metrics":{"Accuracy":"97.5"},"uses_additional_data":false},{"leaderboard":"/sota/intent-detection-on-vietnamese-intent","task":"Intent Detection","dataset":"ATIS (vi)","model":"JointBERT-CAE","rank_in_archive_order":1,"of":1,"metrics":{"Intent Accuracy":"97.7"},"uses_additional_data":false},{"leaderboard":"/sota/intent-detection-on-snips","task":"Intent Detection","dataset":"SNIPS","model":"JointBERT-CAE","rank_in_archive_order":3,"of":10,"metrics":{"Accuracy":"98.3"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-atis","task":"Slot Filling","dataset":"ATIS","model":"JointBERT-CAE","rank_in_archive_order":4,"of":14,"metrics":{"F1":"0.961"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-vietnamese-intent-detection","task":"Slot Filling","dataset":"ATIS (vi)","model":"JointBERT-CAE","rank_in_archive_order":1,"of":1,"metrics":{"Slot F1":"95.5"},"uses_additional_data":false},{"leaderboard":"/sota/slot-filling-on-snips","task":"Slot Filling","dataset":"SNIPS","model":"JointBERT-CAE","rank_in_archive_order":2,"of":10,"metrics":{"F1":"97.0"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}