TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Stock Market Prediction	Astock	ERNIE-SKEP	Accuray	60.66	# 14
Stock Market Prediction	Astock	ERNIE-SKEP	F1-score	60.66	# 14
Stock Market Prediction	Astock	ERNIE-SKEP	Recall	60.59	# 14
Stock Market Prediction	Astock	ERNIE-SKEP	Precision	61.85	# 13

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SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

ACL 2020 · Hao Tian, Can Gao, Xinyan Xiao, Hao liu, Bolei He, Hua Wu, Haifeng Wang, Feng Wu ·

Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.