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- W2968853878 abstract "Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues. However, existing methods either take too many manual efforts (e.g. reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g. sequence-to-sequence methods). In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient. Our method is data-driven and does not require too much manual work for intervention during system design. We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model." @default.
- W2968853878 created "2019-08-22" @default.
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- W2968853878 date "2019-08-14" @default.
- W2968853878 modified "2023-10-16" @default.
- W2968853878 title "Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability" @default.
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- W2968853878 doi "https://doi.org/10.48550/arxiv.1908.05408" @default.
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