言語処理学会 第20回年次大会 発表論文集 (2014年3月)

Anaphora Resolution for Transforming Regular Expressions
into Honorifics in Japanese
Miao Yu, Graham Neubig, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
Graduate School of Information Science
Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma-shi, Nara, Japan{miao-y,neubig,ssakti,tomoki,s-nakamura}@is.naist.jp

1. Introduction
The Japanese language places a heavy emphasis in establishing hierarchical relations among people, or paying respects toelders or those of higher social rank. The Japanese language
includes the normal speech (常語) and honorific speech (敬語), with honorific speech playing an important role in establishing these relationships. The honorifics in Japanese include different levels of respectful (尊敬語), humble (謙譲語), and polite (丁寧語) speech, which are frequently usedin various social or business situations. Because of this, one
verb in Japanese will have at least four ways to express thesame meaning in different situations. For example, word 行
く (go), it will have the four expressions as in Table 1.
The mechanism of honorifics in Japanese is complicated,and many non-native Japanese speakers, as well as membersof the young generations in Japan, have trouble mastering it.
This situation has encouraged the study of automatic systems
that identify the proper form of honorifics in Japanese including automatic translating of learners text into correct honorifics [1, 2]. However these previous works do not have amechanism to determine which kind of honorific form should
be applied in sentences without subjects. As subject omission is a common phenomenon in Japanese, this limits thepractical utility of these methods.
In this paper, we propose the use of anaphora resolution
for this task. We incorporate anaphora resolution into a rulebased machine translation system to translate regular expressions into appropriate honorific of Japanese, and examine theeffectiveness of using correct subjects annotated by a human,
and those automatically predicted by anaphora resolution.

2. Automatic Correction of Japanese
Honorifics

Table 1: Honorific Expressions
Honorific type
Word
Normal (基本形)行く
Respectful (尊敬語)
いらっしゃいます
Humble (謙譲語)
参りますPolite (丁寧語)
行きますthe relationship between the speaker and the subject of thesentence, there will be different honorific forms to be expressed. So, the subject of the sentence plays an importantrole in choosing an appropriate honorific in Japanese.

2.2. Related Work
There has been some related work on automatic processingof honorifics in Japanese. For instance, Noguchi et al. proposes a system to generate all varieties of honorific forms
for single verbs automatically [1]. In their study, verbs are
considered exclusively, and no contextual information is employed. In another study, Shirado et al. proposed a system
for flexibly judging the misusage of honorifics in a Japanese
sentence [2]. The system can point out misused words andphrases, and can also indicate how they are misused. The
advantage of their research is that it can be applied in practical computer aided education. However, there is one major
shortcoming of both works in that both of these works cannot
deal with sentences for which the subject has been omitted.
We investigate this issue further in Section 3.

3. Anaphora Resolution for Honorific
Translation

2.1. Japanese Honorifics

3.1. Honorific Translation Task

The Japanese language has many honorifics, expressions
which show respect, which are used in many social situations. The system of honorifics in Japan is very extensive,
including respectful, humble, polite, and word beautification
expressions. We show an overview in Table 2. Based onJapanese honorifics are used in a wide variety of communication such as dialogue, interviews, meetings and emails. In
this paper, we focus mainly on emails, as it is relative easy
to collect sample data, and a system to correct polite expressions in email would be of great practical use to both foreign-― 476 ―

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Honorific type
Respectful (尊敬語)
Humble (謙譲語)
Polite (丁寧語)Word beautification(美化語)

Table 2: Types of honorifics
Definition
Morphemes
A special form or alternate word Verbs with a higher-up as theused when talking about superiors subject; Nouns
and customers.
Similar to respectful language, but Verbs with a lower rank perused when talking to superiors son as the subject, usually theabout oneself.
speaker; Nouns
Polite speech that doesn’t fall into Verbs with the subject as friends
the above categories. Use of the or other people who’s relationcopula “desu” and the verb ending ship is not clear; Nouns; Adjec“masu”.
tives
Making words more polite or Nouns“beautiful”.
It is commonly
achieved by adding the prefix o- or
go- to a word.

ers and native speakers.

Exampleいらっしゃいます、お返事
お書きします、弊社

帰ります、 美味しいです、
本です

お茶Table 4: Different honorific expressions with different subjects

3.2. Honorific Translation System
In our system, we focus on the correction of verbs, and divide
the processing into four steps:

Subject
higherListener—

Identification of the Reader: As we are considering
emails, in most cases the first line will consist of theaddressee. We use this information to identify thereader of the mail. Next, we check the relative rankof the reader compared to the writer. We do this bychecking if the writer uses titles that indicate higher
rank including “先生、社長、部長、様”. A total of
20 kinds of titles are as chosen higher-ranking titles.
Titles not included in the higher lists, such as “さん” are designated as the same or lower rank than thewriter.

self
self

highersame / lower

Identification of Verbs: In this step, we used Mecab [3], toanalyze every sentence in the document. Based on thisresult, we choose all verbs as potential candidates forcorrection.
Identification of Verb Subjects: In this step, we used theresult of Mecab, to identify nouns in every sentencein the document. Then, we either recognize the firstnoun as the subject of each sentence in the baseline
system, or use anaphora resolution as described in thenext section.
Generation of the Correct Verb Form: In the final step,
we divided the subjects into three categories. When we
recognize the subject of sentence is “私、私たち、うち” and the reader has been identified as higher ranker
we translate into the Humble form, in which the verb
will be written as “お + conjunctive form + いたします”. When we recognize the subject of the sentence― 477 ―

Sentence
今日、大阪にいらっしゃいます。
今日、大阪に参ります。
今日、大阪に行きます。

is higher-ranking than the writer, we translate into Respectful forms, in which the verb will be written as “お+ conjunctive form + になります”. All other subjects
are recognized to be Polite forms, in which the verb
will be written as “conjunctive form + ます”. Then,
we choose the correct verb form based on the subject
and context of each verb. Based on our analysis of thedata, we also identified a number of instances that do
not fit in the above framework. Those are added as
rules as shown in Table 3.
3.3. Anaphora Resolution
As we explained in Section 3.1, based on different subjects, we will determine different honorific forms. However,Japanese is a pro-drop language, which means that the subject of the sentence can often be omitted. But for different
subjects in the same sentence, there will be different expressions for the honorifics. For example, in the sentence “今日、大阪に行く。”, based on the latent subject, we will use different expressions, as shown in Table 4. This shows that it isimportant to identify a subject in sentence before we translate
it into appropriate honorific forms in Japanese.
Zero-anaphora resolution is a technology that has theability to identify dropped subjects in these situations. For

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Table 3: Additional rules to handle exceptions in honorific translation
Rules
1
2
3
4
5
6
7

When the subject does not include the speaker and if the input has “しましょう”, the output will be changed into “します”.
When the subject is the speaker and if the input has “おる”, the output will be changed into “いる”.
If the sentence has “ますです” and the output will be deleted “です”.
If the sentence has “しよ、しましょ” and the output will be changed into “しよう” or “しましょう”
When the sentence has “ます、です、する” before “、” add “が” before “、”.
If the sentence has “よる、から、ので、” and the verb before will not be translated.
If the subject is of higher rank “よろしいでしょうか” will be translated into “いかがでしょうか”.

zero-anaphora resolution, Iida et al. approached this problem
by decomposing it into intra-sentential and inter-sentential
zero anaphora [4]. They improved the accuracy of intasentential zero-anaphora, which consequently improved theoverall performance of zero-anaphora resolution. In anotherstudy, Yeh et al. used shallow parsing instead of complex
parsing to resolve zero anaphora. In their study, the precision
rates of zero anaphora detection and the recall rate of zero
anaphora resolution with the method are 81% and 70% respectively [5]. In our system, we choose to adapt the methodof Iida et al. as it provides high performance and is available
open source in the SynCha package [6].

Table 5: WER results on Source (WER is 36.05%)
No Resolution SynCha
Human+Res+Hum
35.88% 35.53% 35.70%+Res-Hum
35.53% 35.18% 35.01%-Res+Hum
36.05% 36.22% 36.40%-Res-Hum
35.53% 35.53% 35.53%

We also evaluated four different options regarding whichforms to translate.

4. Experiment+Res+Hum: translate into appropriate respectful, humble,
and polite expressions

In the experiment, we evaluated the performance of themethod proposed in Section 3.

+Res-Hum: only translate into respectful and polite expressions

4.1. Experiment Setting-Res+Hum: only translate into humble and polite expressions

As data for our experiment, we collect 6 emails written
by non-native speakers as the source data for the proposed
method. For the reference data, we asked a native speaker ofJapanese well-versed in business to correct the source data
into proper and polite Japanese.
We divided the source data into 2 types. The first type
(Source) is regular source data which has mistakes of all varieties including verbs, nouns, and others. The other type
(KeigoOnly) is source data that is modified manually so thatthe only mistakes are misuse of polite expressions. We do
this because our system focuses on translating into appropriate honorifics of Japanese, so the second setting removes theeffect of mistakes that are not the target of our system. We
prepare three types of systems to compare:
No Resolution: translate the source data without resolving
dropped subjects
SynCha: automatically resolve omitted subjects using SynCha and consider them when translating into honorifics
Human: annotate omitted subjects by hand and then consider them when translating into honorifics― 478 ―

-Res-Hum: only translate into polite expressions
We measure the accuracy of our method using word error
rate (WER) using the corrected text as a reference.
4.2. Evaluation and Results
First, we show the results of the experiment using the Source
data as input in Table 5. The WER of Source is 36.05%.
Table 5 shows that almost all forms of the system achieve
better performance than the original source. Comparing accuracy of No Resolution, SynCha, and Human, we confirm
that first resolving the omission of subjects, and then translating into appropriate honorifics of Japanese is useful andnecessary. In addition the WER of the systems that generate humble expressions is higher than those that do not. Thisis because it is common to use humble forms only in someoccasions such as when performing an action for a superior.
We also show the results of the experiment using the KeigoOnly data as input in Table 6. The WER of KeigoOnly is
15.25%. This Table 6 shows that the tendency is similar towhen considering all errors.

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Table 7: Examples of each system’s output, with the previous sentence of context
社長がお参加になりますか？
日本料理店でよろしいでしょうか？
日本料理店でいかがでしょうか？
今年４月に大連理工大学を卒業した、素晴らしい技術を勉強するために、日本に留学来ました。
No Resolution / SynCha 先生のウェーブページを見て、ご研究の音声翻訳が気になります。
Human
先生のウェーブページを拝見して、ご研究の音声翻訳が気になります。
Context
No Resolution
SynCha / Human
Context

Table 6: WER results on KeigoOnly (WER is 15.25%)
No Resolution SynCha
Human+Res+Hum
14.73% 14.38% 14.38%+Res-Hum
14.38% 14.04% 13.86%-Res+Hum
14.90% 14.90% 15.08%-Res-Hum
14.38% 14.38% 14.38%

4.3. Discussion
We show some examples of the system output in Table 7. In
the first, because the preceding sentence has the subject “社長”, SynCha and the Human annotator recognize the subjectof “よろしい” to be “社長”. Thus, both are able to translate the “よろしいでしょうか” into “いかがでしょうか”.
However, in the second example, because the context does
not directly include the subject “私”, SynCha was not able toidentify the subject appropriately, resulting in failure to translate into the appropriate honorifics. Identifying anaphora thatrefer to the reader or writer of a passage is a difficult task,
but Hangyo et al. have recently proposed a method that may
solve this problem [7].
It should be noted, however, that the number of corrections made by our model are somewhat small compared tothe total number of phenomena to be corrected in KeigoOnly.
One reason for this is that there are often multiple ways to express verbs politely in Japanese. For example, in some places
our annotator changed “なりたいです” into “なりたいと思います”. There are many such examples that our system cannot handle at this time.
Finally, nouns, adjectives, and other expressions also
have honorific forms. Because we are only translating verbs,
it is not sufficient to translate all of the honorific expressions
in our corpus.

phemes other than only verbs. We will also improve thetranslating performance of humble expressions.

6. References[1] 野口聡, 南條浩輝, and 吉見毅彦. 動詞の通常表現から敬語表現への換言. In 言語処理学会第 13 回年次大会(NLP2007), 2007.
[2] Tamotsu Shirado, Satoko Marumoto, Masaki Murata,and Hitoshi Isahara. System for flexibly judging the misuse of honorifics in Japanese. In Proceedings of PacificAsia Conference on Language, Information, and Computation, pages 503–510, 2011.
[3] Taku Kudo. MeCab: yet another part-of-speech and morphological analyzer. 2006.
[4] Ryu Iida, Kentaro Inui, and Yuji Matsumoto. Exploiting
syntactic patterns as clues in zero-anaphora resolution.
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Computational Linguistics and 44th Annual Meeting of
the ACL, pages 625–632, 2006.
[5] Ching-Long Yeh and Yi-Chun Chen. Zero anaphora resolution in Chinese with shallow parsing. In Journal of
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[6] Ryu Iida and Massimo Poesio. A cross-lingual ILP solution to zero anaphora resolution. In Proceedings ofthe 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies- Volume 1, 2011.
[7] Masatsugu Hangyo, Daisuke Kawahara, and Sadao
Kurohashi. Japanese zero reference resolution considering exophora and author/reader mentions. In Proceedings of Empirical Methods in Natural Language Processing, pages 924–934, 2013.

5. Conclusion
We proposed a method to incorporate anaphora resolution
with a rule-based system to translate the regular expressions
into appropriate honorifics of Japanese. Experimental results
showed that this improved the performance of the honorifictranslation system.
In future research, we plan to expand to varieties of mor-

― 479 ―

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All Rights Reserved.