Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EmotionX-KU: BERT-Max based Contextual Emotion Classifier   We propose a contextual emotion classifier based on a transferable language
model and dynamic max pooling, which predicts the emotion of each utterance in
a dialogue. A representative emotion analysis task, EmotionX, requires to
consider contextual information from colloquial dialogues and to deal with a
class imbalance problem. To alleviate these problems, our model leverages the
self-attention based transferable language model and the weighted cross entropy
loss. Furthermore, we apply post-training and fine-tuning mechanisms to enhance
the domain adaptability of our model and utilize several machine learning
techniques to improve its performance. We conduct experiments on two
emotion-labeled datasets named Friends and EmotionPush. As a result, our model
outperforms the previous state-of-the-art model and also shows competitive
performance in the EmotionX 2019 challenge. The code will be available in the
Github page.

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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: eess