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- W4384303184 abstract "In the realm of automatic question-answering (Q&A) for technical communities, accurately perceiving and predicting user intent is a crucial step towards improving Q&A system performance by integrating user intention with answer reasoning processes. We conducted research into intent understanding at the sentence level, aiming to clarify the function of each sentence in technical Q&A communities and improve the system's response accuracy. To address the shortcomings of existing research, which typically ignores information such as speaker type and sentence position, we propose a multi-task learning framework to effectively utilize this information for sentence representation learning. By doing so, the model can acquire richer interactive question-answer language features, thereby enhancing the performance of intent label classification. Within this framework, we present two models: BA-multi and CCR-multi. Our validation experiments on the MSDialog-Intent dataset demonstrate that the multi-task learning model significantly outperforms both the baseline and feature extension models, achieving state-of-the-art performance." @default.
- W4384303184 created "2023-07-15" @default.
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- W4384303184 date "2023-01-01" @default.
- W4384303184 modified "2023-09-27" @default.
- W4384303184 title "Intent Understanding for Automatic Question Answering in Network Technology Communities Based on Multi-task Learning" @default.
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- W4384303184 doi "https://doi.org/10.1007/978-3-031-36822-6_10" @default.
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