Cluster-based Prediction of User Ratings for Stylistic Surface Realisation
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation
复制标题
基于聚类的用户评分预测以实现风格表面
DOI：
10.3115/v1/e14-1074
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Oliver Lemon
中科院分区：
文献类型：
--
作者：
Nina Dethlefs;H. Cuayáhuitl;H. Hastie;Verena Rieser;Oliver Lemon
表面实现通常取决于它们的目标风格和受众。从数据中估计风格实现者的一个挑战是，人类对语言形式和风格的主观感知差异很大，导致对同一话语的评分几乎没有相关性。我们分两个步骤解决这个问题。首先，我们估计了话语语料库的语言特征与其人类风格评级之间的映射函数。根据用户评分的相似度将用户划分为簇，这样就可以估计新语音的评分，甚至对于新的未知用户也是如此。在第二步中，估计的模型被用来重新排序许多表面实现器的输出，以产生风格自适应的输出。结果证实，生成的风格对人类法官来说是可识别的，并且基于用户群的预测模型比基于平均用户群的模型产生更好的评级预测。
Surface realisations typically depend on their target style and audience. A challenge in estimating a stylistic realiser from data is that humans vary significantly in their subjective perceptions of linguistic forms and styles, leading to almost no correlation between ratings of the same utterance. We address this problem in two steps. First, we estimate a mapping function between the linguistic features of a corpus of utterances and their human style ratings. Users are partitioned into clusters based on the similarity of their ratings, so that ratings for new utterances can be estimated, even for new, unknown users. In a second step, the estimated model is used to re-rank the outputs of a number of surface realisers to produce stylistically adaptive output. Results confirm that the generated styles are recognisable to human judges and that predictive models based on clusters of users lead to better rating predictions than models based on an average population of users.
影响因子：
2.5
作者：
A. Belz
通讯作者：
A. Belz
使用动态用户建模的对话系统中的自适应生成
影响因子：
9.3
作者：
Janarthanam S
通讯作者：
Janarthanam S