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<?xml version="1.0" standalone="yes"?> <Paper uid="A00-1006"> <Title>Translation using Information on Dialogue Participants</Title> <Section position="1" start_page="0" end_page="0" type="abstr"> <SectionTitle> Abstract </SectionTitle> <Paragraph position="0"> 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.</Paragraph> <Paragraph position="1"> ture that uses '% pragmatic adaptation&quot; (LuperFoy and others, 1998), and Mima et al. proposed a method that uses &quot;situational information&quot; (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.</Paragraph> <Paragraph position="2"> We have been paying special attention to &quot;politeness,&quot; 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 &quot;politeness&quot; 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%.</Paragraph> <Paragraph position="3"> These rates could be improved to 86% and 96%, respectively (see Section 4). It is therefore possible to use a &quot;participant's social role&quot; (a clerk or a customer in this case) to appropriately make the translation results &quot;polite,&quot; 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 Introduction 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.</Paragraph> <Paragraph position="4"> Several dialogue translation methods that use extra-linguistic information have been proposed. Horiguchi outlined how &quot;spoken language pragmatic information&quot; 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 fitranslation system. Section 4 shows an experiment and results, followed by a discussion in Section 5. Finally, Section 6 concludes this paper. 2 A</Paragraph> <Section position="1" start_page="0" end_page="0" type="sub_section"> <SectionTitle> Participant's Politeness Social Role and </SectionTitle> <Paragraph position="0"> A Method of Using Information on Dialogue Participants This section focuses on one participant's social role. We investigated Japanese outputs of a dialogue translation system to see how many utterances should be polite expressions in a current translation system for travel arrangement. We input 1,409 clerk utterances into a Transfer Driven Machine Translation system (Sumita and others, 1999) (TDMT for short). The inputs were closed utterances, meaning the system already knew the utterances, enabling the utterances to be transferred at a good quality. Therefore, we used closed utterances as the inputs to avoid translation errors. As a result, it was shown that about 70% (952) of all utterances should be improved to use polite expressions. This result shows that a current translation system is not enough to make a conversation smoothly. Not surprisingly, if all expressions were polite, some Japanese speakers would feel insulted. Therefore, Japanese speakers do not have to use polite expression in all utterances. We classified the investigated data into different types of English expressions for Japanese politeness, i.e., into honorific titles, parts of speech such as verbs, and canned phrases, as shown in Table 1; however, not all types appeared in the data. For example, when the clerk said &quot;How will you be paying, Mr. Suzuki,&quot; the Japanese translation was made polite as &quot;donoyouni oshiharaininarimasu-ka suzuki-sama&quot; in place of the standard expression &quot;donoyouni shiharaimasu-ka suzuki-san.&quot; Table 1 shows that there is a difference in how expressions should be made more polite according to the type, and that many polite expressions can be translated by using only local information, i.e., transfer rules and dictionary entries. In the next section, we describe how to incorporate the information on dialogue participants, such as roles and genders, into transfer rules and dictionary entries in a dialogue translation system.</Paragraph> <Paragraph position="1"> This section describes how to use information on dialogue participants, such as participants' social roles and genders. First, we describe T D M T , which we also used in our experiment. Second, we mention how to modify transfer rules and transfer dictionary entries according to information on dialogue participants.</Paragraph> </Section> <Section position="2" start_page="0" end_page="0" type="sub_section"> <SectionTitle> 3.1 Transfer Driven Machine Translation </SectionTitle> <Paragraph position="0"> T D M T uses bottom-up left-to-right chart parsing with transfer rules as shown in Figure 1. The parsing determines the best structure and best transferred result locally by performing structural disambiguation using semantic distance calculations, in parallel with the derivation of possible structures. The semantic distance is defined by a thesaurus. (source pattern) ==~ J((target pattern 1) ((source example 1) (source example 2) target pattern, and a source example. The source pattern consists of variables and constituent boundaries (Furuse and Iida, 1996). A constituent boundary is either a functional word or the part-of-speech of a left constituent's last word and the part-of-speech of a right constituent's first word. In Example (1), the constituent boundary IV-CN) is inserted between &quot;accept&quot; and &quot;payment,&quot; because &quot;accept&quot; is a Verb and &quot;payment&quot; is a C o m m o n Noun. The target pattern consists of variables that correspond to variables in the source pattern and words of the target language. The source example consists of words that come from utterances referred to when a person creates transfer rules (we call such utterances closed utterances). Figure 2 shows a transfer rule whose source pattern is (X (V-CN) Y). Variable X corresponds to x, which is used in the target pattern, and Y corresponds to y, which is also Type: Eng: Standard: Polite: Gloss: Suzuki donoyouni shiharaimasu-ka suzuki-san donoyouni o_shiharaininarimasu-ka suzuki-sama How pay-QUESTION verb, c o m m o n n o u n suzuki-Mr.</Paragraph> <Paragraph position="1"> Type: Eng: Standard: Polite: Gloss: Type: Eng: Standard: Polite: Gloss: Type: Eng: Standard: Polite: Gloss: Type: Eng: Standard: Polite: Gloss: We have two types of rooms available aiteiru ni-shurui-no heya-ga aiteiru used in the target pattern. The source example ((&quot;accept&quot;) (&quot;payment&quot;)) comes from Example (1), and the other source examples come from the other closed utterances. This transfer rule means that if the source pattern is (X (VC N ) Y) then (y &quot;wo&quot; x) or (y &quot;ni&quot; x) is selected as the target pattern, where an input word pair corresponding to X and Y is semantically the most similar in a thesaurus to, or exactly the same as, the source example. For example, if an input word pair corresponding to X and Y is semantically the most similar in a thesaurus to, or exactly the same as, ((&quot;accept&quot;) (&quot;payment&quot;)), then the target pattern (y &quot;wo&quot; x) is selected in Figure 2. As a result, an appropriate target pattern is selected. After a target pattern is selected, T D M T creates a target structure according to the pattern</Paragraph> <Paragraph position="3"> in Figure 3. If the input is &quot;accept ( V - C N ) payment,&quot; then this part is translated into &quot;shiharai wo uketsukeru.&quot; &quot;wo&quot; is derived from the target pattern (y &quot;wo&quot; x), and &quot;shiharai&quot; and &quot;uketsukeru&quot; are derived from the transfer dictionary, as shown in Figure 4.</Paragraph> <Paragraph position="4"> Figure 5: Transfer rule format with information on dialogue participants &quot;target pattern 11&quot; and the source word &quot;source example 1&quot; are used to change the translation according to information on dialogue participants. For example, if &quot;:pattern-cond 11&quot; is defined as &quot;:h-gender male&quot; as shown in Figure 7, then &quot;target pattern 11&quot; is selected when the hearer is a male, that is, &quot;(&quot;Mr.&quot; x)&quot; is selected. Moreover, if &quot;:word-cond 11&quot; is defined as &quot;:srole clerk&quot; as shown in Figure 8, then &quot;source example 1&quot; is translated into &quot;target word 11&quot; when the speaker is a clerk, that is, &quot;accept&quot; is translated into &quot;oukesuru.&quot; Translations such as &quot;target word 11&quot; are valid only in the source pattern; that is, a source example might not always be translated into one of these target words. If we always want to produce translations according to information on dialogue participants, then we need to modify the entries in the transfer dictionary like Figure 6 shows. Conversely, if we do not want to always change the translation, then we should not modify the entries but modify the transfer rules. Several conditions can also be given to &quot;:word-cond&quot; and &quot;:pattern-cond.&quot; For example, &quot;:s-role customer and :s-gender female,&quot; which means the speaker is a customer and a female, can be given. In Figure 5, &quot;:default&quot; means the de- null (&quot;Ms.&quot; x) :h-gender female (&quot;Mr-ms.&quot; x)) ((&quot;room number&quot;))) ) Figure 7: Transfer rule example with the participant's gender 3.2 Transfer R u l e s a n d E n t r i e s according to Information on Dialogue Participants For this research, we modified the transfer rules and the transfer dictionary entries, as shown in Figures 5 and 6. In Figure 5, the target pattern fifault target pattern or word if no condition is matched. The condition is checked from up to down in order; that is, first, &quot;:pattern-cond 11,&quot; second, not have rules and entries for pattern conditions and word conditions according to another participant's information, such as &quot;:s-role customer'(which means the speaker's role is a customer) and &quot;:s-gender male&quot; (which means the speaker's gender is male), T D M T can translate expressions corresponding to this information too. For example, &quot;Very good, please let me confirm them&quot; will be translated into &quot;shouchiitashimasita kakunin sasete itadakimasu&quot; when the speaker is a clerk or &quot;soredekekkoudesu kakunin sasete kudasai&quot; when the speaker is a customer, as shown in Example (2). By making a rule and an entry like the examples shown in Figures 8 and 9, the utterance of Example (1) will be translated into &quot;watashidomo wa kurejitto kaado deno oshiharai wo oukeshimasu&quot; when the speaker is a clerk. 4 An Experiment improve the level of &quot;politeness.&quot; We conducted an experiment using the transfer rules and transfer dictionary for a clerk with 23 unseen dialogues (344 utterances). Our input was off-line, i.e., a transcription of dialogues, which was encoded with the participant's social role. In the on-line situation, our system can not infer whether the participant's social role is a clerk or a customer, but can instead determine the role without error from the interface (such as a microphone or a button). In order to evaluate the experiment, we classifted the Japanese translation results obtained for the 23 unseen dialogues (199 utterances from a clerk, and 145 utterances from a customer, making 344 utterances in total) into two types: expressions that had to be changed to more polite expressions, and expressions that did not. Table 2 shows the number of utterances that included an expression which had to be changed into a more polite one (indicated by &quot;Yes&quot;) and those that did not (indicated by &quot;No&quot;). We neglected 74 utterances whose translations were too poor to judge whether to assign a &quot;Yes&quot; or &quot;No.&quot; Table 2: The number of utterances to be changed or not they are ignored in this paper.</Paragraph> <Paragraph position="5"> The T D M T system for English-to-Japanese at the time Of the experiment had about 1,500 transfer rules and 8,000 transfer dictionary entries. In other words, this T D M T system was capable of translating 8,000 English words into Japanese words. About 300 transfer rules and 40 transfer dictionary entries were modified to The translation results were evaluated to see whether the impressions of the translated results were improved or not with/without modification for the clerk from the viewpoint of &quot;politeness.&quot; Table 3 shows the impressions obtained according to the necessity of change shown in Table 2. The evaluation criteria are recall and precision, which are defined as follows: Recall = number of utterances whose impression is better number of utterances which should be more worse: Impression of a translation is worse. no-diff: There is no difference between the two translations.</Paragraph> <Paragraph position="6"> Precision = number of utterances whose impression is better number of utterances whose expression has been changed by the modified rules and entries The recall was 65% (= 68 - (68 + 5 + 3 + 28)) and the precision was 86% (= 68 -: (68 + 5 +</Paragraph> </Section> </Section> class="xml-element"></Paper>