基于模式匹配网络的鲁棒跨域不流畅检测
计算与语言
2018-11-20 v1 人工智能
摘要
本文引入一种新颖的模式匹配神经网络架构，其使用邻域相似度得分作为特征，从而在不流畅检测任务中免去特征工程之需。我们在四种不同语音体裁的不流畅检测上评估该方法，表明在域内数据上其效果与手工设计的模式匹配特征相当，并在跨域场景中取得更优性能。
引用
@article{arxiv.1811.07236,
title = {Robust cross-domain disfluency detection with pattern match networks},
author = {Vicky Zayats and Mari Ostendorf},
journal= {arXiv preprint arXiv:1811.07236},
year = {2018}
}
备注
This paper was submitted to EMNLP 2018 and was rejected. Our EMNLP submission is posted here to establish concurrency with "Disfluency Detection using Auto-Correlational Neural Networks" by P. Lou, P. Anderson, M. Johnson which was submitted to EMNLP at the same time