语用学研究与非任务对话技术

E. Gilmartin, Christian Saam
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引用次数: 2

摘要

人们对对话系统越来越感兴趣,这种对话系统使用户参与对话,而不是完全集中于立即执行明确定义的实际任务。人们对对话系统设计的数据驱动方法也越来越感兴趣,受机器翻译领域成功的启发,人们越来越关注序列到序列的深度学习模型。在这篇论文中,我们讨论了关于随意谈话或社交谈话的研究以及当前系统设计的方法,突出了需要和解的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pragmatics Research and Non-task Dialog Technology
Interest is growing in dialog systems which engage users in conversations that are not entirely focussed on the immediate performance of clearly defined practical tasks. Interest is also growing in data-driven methods for dialog system design, with increasing focus on sequence-to-sequence deep learning models, inspired by success in the machine translation sphere. In this position paper, we discuss research on casual conversation or social talk and current methods in design of systems, highlighting areas which need reconciliation.
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