Linguistic Individuality Transformation for Spoken Language
Linguistic Individuality Transformation for Spoken Language
复制标题
口语的语言个性转化
DOI:
10.1007/978-3-319-19291-8_13
复制
发表时间:
2015
影响因子:
2.3
通讯作者:
Satoshi Nakamura
中科院分区:
文献类型:
--
作者:
M. Mizukami;Graham Neubig;S. Sakti;T. Toda;Satoshi Nakamura
In text and speech, there are various features that express the individuality of the writer or speaker. In this paper, we take a step towards the creation of dialogue systems that consider this individuality by proposing a method for transforming individuality using a technique inspired by statistical machine translation (SMT). However, finding a parallel corpus with identical semantic content but different individuality is difficult, precluding the use of standard SMT techniques. Thus, in this paper, we focus on methods for creating a translation model (TM) using techniques from the paraphrasing literature and a language model (LM) by combining small amounts of individuality-rich data with larger amounts of background text. We perform an automatic and manual evaluation comparing the effectiveness of three types of TM construction techniques and find that the proposed system using a method focusing on a limited set of function words is most effective and can transform individuality to a degree that is both noticeable and identifiable.
影响因子:
4.3
作者:
G. Neubig;Y. Akita;S. Mori;and T. Kawahara
通讯作者:
and T. Kawahara
影响因子:
0.8
作者:
Kinsui;Satosi;Teshigawara;Mihoko
通讯作者:
Mihoko