How to talk to strangers: Generating medical reports for first-time users
How to talk to strangers: Generating medical reports for first-time users
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DOI：
10.1109/fuzz-ieee.2016.7737739
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Dimitra Gkatzia;Verena Rieser;Oliver Lemon
中科院分区：
文献类型：
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作者：
Dimitra Gkatzia;Verena Rieser;Oliver Lemon
我们提出了一种新的方法来处理首次用户的情况下，从时间序列数据在健康领域的自动报告生成。处理第一次使用的用户是自然语言生成（NLG）和交互式系统的一个常见问题-系统无法在没有事先交互或用户知识的情况下适应用户。在本文中，我们提出了一种新的框架，为首次使用的用户生成医疗报告，使用多目标优化（MOO）来考虑多种可能的用户类型的偏好，其中潜在用户的内容偏好建模为目标函数。我们提出的方法优于两个有意义的基线在与潜在用户的评估，产生大（= .79）和中等（= .46）的效果大小分别。
We propose a novel approach for handling first-time users in the context of automatic report generation from time-series data in the health domain. Handling first-time users is a common problem for Natural Language Generation (NLG) and interactive systems in general - the system cannot adapt to users without prior interaction or user knowledge. In this paper, we propose a novel framework for generating medical reports for first-time users, using multi-objective optimisation (MOO) to account for the preferences of multiple possible user types, where the content preferences of potential users are modelled as objective functions. Our proposed approach outperforms two meaningful baselines in an evaluation with prospective users, yielding large (= .79) and medium (= .46) effect sizes respectively.