Translation using Information on Dialogue Participants

This paper proposes a way to improve the translation quality by using information on dialogue participants that is easily obtained from outside the translation component. We incorporated information on participants' social roles and genders into transfer rules and dictionary entries. An experiment with 23 unseen dialogues demonstrated a recall of 65% and a precision of 86%. These results showed that our simple and easy-to-implement method is effective, and is a key technology enabling smooth conversation with a dialogue translation system. 1 I n t r o d u c t i o n Recently, various dialogue translation systems have been proposed (Bub and others, 1997; Kurematsu and Morimoto, 1996; Rayner and Carter, 1997; Ros~ and Levin, 1998; Sumita and others, 1999; Yang and Park, 1997; Vidal, 1997). If we want to make a conversation proceed smoothly using these translation systems, it is important to use not only linguistic information, which comes from the source language, but also extra-linguistic information, which does not come from the source language, but, is shared between the participants of the conversation. Several dialogue translation methods that use extra-linguistic information have been proposed. Horiguchi outlined how "spoken language pragmatic information" can be translated (Horiguchi, 1997). However, she did not apply this idea to a dialogue translation system. LuperFoy et al. proposed a software architec*Current affiliation is ATR Spoken Language Translation Research Laboratories Current mail addresses are { setsuo.yarnada, eiichiro.sumita, hideki.kashioka} @slt. atr. co.jp ture that uses '% pragmatic adaptation" (LuperFoy and others, 1998), and Mima et al. proposed a method that uses "situational information" (Mima and others, 1997). LuperFoy et al. simulated their method on man-machine interfaces and Mima et al. preliminarily evaluated their method. Neither study, however, applied its proposals to an actual dialogue translation system. The above mentioned methods will need time to work in practice, since it is hard to obtain the extra-linguistic information on which they depend. We have been paying special attention to "politeness," because a lack of politeness can interfere with a smooth conversation between two participants, such as a clerk and a customer. It is easy for a dialogue translation system to know which participant is the clerk and which is the customer from the interface (such as the wires to the microphones). This paper describes a method of "politeness" selection according to a participant's social role (a clerk or a customer), which is easily obtained from the extra-linguistic environment. We incorporated each participant's social role into transfer rules and transfer dictionary entries. We then conducted an experiment with 23 unseen dialogues (344 utterances). Our method achieved a recall of 65% and a precision of 86%. These rates could be improved to 86% and 96%, respectively (see Section 4). It is therefore possible to use a "participant's social role" (a clerk or a customer in this case) to appropriately make the translation results "polite," and to make the conversation proceed smoothly with a dialogue translation system. Section 2 analyzes the relationship between a particular participant's social role (a clerk) and politeness in Japanese. Section 3 describes our proposal in detail using an English-to-Japanese