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- W2806801285 abstract "This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multi-task learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set." @default.
- W2806801285 created "2018-06-13" @default.
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- W2806801285 date "2018-01-01" @default.
- W2806801285 modified "2023-09-27" @default.
- W2806801285 title "deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets" @default.
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- W2806801285 doi "https://doi.org/10.18653/v1/s18-1034" @default.
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