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arXiv · 2110.07310

Solving Aspect Category Sentiment Analysis as a Text Generation Task

Jian Liu·Zhiyang Teng·Leyang Cui·Hanmeng Liu·Yue Zhang
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Abstract

Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.

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Jian Liu, Zhiyang Teng, Leyang Cui, Hanmeng Liu, Yue Zhang. 2021-10-14. Solving Aspect Category Sentiment Analysis as a Text Generation Task. https://arxiv.org/abs/2110.07310

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