PAI at SemEval-2023 Task 4: A general multi-label classification system
with class-balanced loss function and ensemble module. We employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value
Detection shared task.

PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module

PAI at SemEval-2023 Task 2: A Universal System for Named Entity
Recognition with External Entity Information

PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information

To view Day 2 Live Session Recording click [**here**](https://underline.io/events/395/sessions?eventSessionId=15300&searchGroup=lecture) SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. Each year's workshop features a collection of shared tasks in which computational semantic analysis systems designed by different teams are presented and compared.

SemEval is sponsored by the SIGLEX Special Interest Group on the Lexicon of the Association for Computational Linguistics. For more information on SemEval and its history, please see http://en.wikipedia.org/wiki/SemEval.
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W1: The 17th International Workshop on Semantic Evaluation (SemEval)_Day 1

workshop paper

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PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module

PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information

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