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首页> 外文会议>Annual conference of the North American Chapter of the Association for Computational Linguistics: human language technologies;International workshop on semantic evaluation >SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
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SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification

机译：SNUES在SemEval-2019上的任务3：解决会话分类中的培训考试类别分布不匹配

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摘要

We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation.
机译：我们通过扩展现有的解决班级不平衡问题的方法，提出了几种技术来解决SemEval 2019的情境情感检测任务中训练数据和测试数据之间的班级分配不匹配问题。减小了预测分布和真实性之间的距离，它们始终对性能表现出积极的影响。我们还提出了一种新颖的神经体系结构，该体系结构利用了整体语境以及每种话语的表示。这些方法和模型的组合在最终评估中获得了约0.766的微型F1分数。

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