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• W2979155592 abstract "In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA." @default.
• W2979155592 created "2019-10-10" @default.
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• W2979155592 date "2019-10-02" @default.
• W2979155592 modified "2023-09-24" @default.
• W2979155592 title "Exploiting BERT for End-to-End Aspect-based Sentiment Analysis" @default.
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• W2979155592 doi "https://doi.org/10.48550/arxiv.1910.00883" @default.
• W2979155592 hasPublicationYear "2019" @default.
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