Applicability Analysis of Corpus-derived Paraphrases
toward Example-based Paraphrasing
Kiyonori Ohtake††ATR Spoken Language Translation
Research Laboratories
2-2-2 Hikaridai, Seika-cho,
Soraku-gun, Kyoto 619-0288 Japankiyonori.ohtake@atr.co.jp

Kazuhide Yamamoto†‡‡Department of Electrical Engineering,
Nagaoka University of Technology
1603-1 Kamitomioka, Nagaoka,
Niigata 940-2188 Japan
yamamoto@fw.ipsj.or.jp

Abstract
Two kinds of paraphrases extracted from a bilingual parallel corpus were analyzed.
One is from an adjectival predicate sentence to a non-adjectival one. The other is froma passive form to a non-passive form. The ability to extract paraphrases is strongly
desired for paraphrasing studies. Although extracting paraphrases from multi-lingual
parallel corpora is possible, the type of paraphrases extracted is unknown. We discovered what types of examples can be obtained, and what types of paraphrasing will beavailable for the two kinds of paraphrases.
1 Introduction
Paraphrasing is a very important form of processing for natural language processing (NLP).
A characteristic property of natural language is that various expressions can exist to express asingle concept. So far, ad hoc paraphrasing methods have been applied to many NLP applications, such as machine translation, information retrieval, automatic summarization, and so on.
Nowadays, however, many researchers recognize automatic paraphrasing as a very important
technique for many areas of NLP, and work in this field has recently become more active.
We view paraphrasing as a translation where the source language and the target language are
the same. Thus, we can take a similar approach to that of machine translation for paraphrasing.
In this study, we use an example-based approach to control paraphrasing.
So far, a number of new methods for paraphrase extraction have emerged (e.g., (Lin and Pantel, 2001; Yamamoto, 2002; Barzilay and McKeown, 2001; Barzilay and Lee, 2003; Shinyama
and Sekine, 2003; Pang et al., 2003)). There are several extracting methods: for example,
one extracts paraphrases from a monolingual corpus, and another extracts from a multilingual
parallel corpus. However, most of these methods have focused on efficiency or how many paraphrases would be extracted, and there have been few discussions on the types of paraphrases
that were extracted.
Meanwhile, a rule-based approach seems reasonable in terms of the application of paraphrasing (Takahashi et al., 2001). Therefore, an investigation of how many paraphrasing examples
are valuable, or difficult-to-write paraphrasing rules that cover the extracted examples are important. In this paper, we thus discuss the types of paraphrases that are extracted from a bilingual
parallel corpus.
We analyze paraphrasing examples extracted from a Japanese-English parallel corpus oftravel conversations. The corpus is made for Japanese speakers traveling to English-speaking
countries. We can extract paraphrases from a parallel corpus (Barzilay and McKeown, 2001).
380

The basic idea of this method is that one expression can be translated into several expressions,and the translated expressions are paraphrases of each other.
In this paper, we introduce a method that extracts paraphrasing examples from a JapaneseEnglish parallel corpus. Then, we analyze the extracted examples and discuss several issues
about them. The purpose of this study is to answer the following questions:(a) How many of the paraphrasing examples that are automatically extracted from a parallel
corpus can be directly applied to paraphrasing?
(b) What types of paraphrasing will be achieved by the extracted examples?
2 Targets of Analysis
We analyzed two kinds of paraphrases. One is from an adjectival predicate sentence to a nonadjectival one. The other is from a passive formed sentence to a non-passive formed one. We
focused on these two kinds of paraphrases because adjectival predicate sentences are used invarious situations with various meanings, and passive formed sentences are also frequently
used in Japanese to express several meanings.
Adjectival predicate expressions and passive formed expressions are frequently used in spoken language because they can express several meanings with one expression, and they are easy
to use. On the contrary, such expressions are difficult for computers to handle correctly because
they can convey several meanings. Thus, there are great advantages in being able to paraphrase
adjectival predicate expressions and passive formed expressions into non-adjectival predicate
expressions and non-passive formed expressions, respectively.
2.1 Adjectival predicates
The paraphrasing examples for the target of this analysis are paraphrases from sentences thathave an adjectival predicate at the sentence-end to sentences that have a non-adjectival predicate
at the sentence-end. In this paper, a sentence that has an adjectival predicate at the sentenceend is called an adjectival predicate sentence. An adjectival predicate sentence includes such
expressions as “ie kekkou desu (no thank you)”, which uses the adjectival noun (kekkou) +copula (desu).
For example, the functional role of “... ii1 desu ka(question particle) ” may be classified as follows:
confirmation: kore(this) de(particle) ii desu ka. (Is this all right?)
asking for permission: tabako(cigarette) o(particle) sutte(smoke) mo(particle) ii desu ka. (May I smoke?)asking a way: donoyouni(how) kake(write) ba(particle) ii desu ka. (How do I write this?2 )asking one’s favorite/taste: donna(what) iro(color) ga(particle) ii desu ka. (What color do you like?)
The definition of an adjectival predicate sentence is as follows:a sentence that has a morpheme where the part of speech (POS) is an adjective or
adjectival noun in its last predicate phrase.
1
2

The fundamental meaning of ii is good.
A more correct translation might be “How do I fill in this form?”381

2.2 Passive forms
In Japanese, passive forms convey several meanings. From only surface information, it is difficult to determine whether a passive form is used as an honorific term or truly as a passive
form. In addition, passive forms sometimes express the possibility of an action. For example,“mirareru (to be able to see, to see, or to be seen) ” expresses the following three types ofmeaning:• to express possibility“yama wa mirare masita ka (Could you see the mountain?),”• to express an honorific“yama o mirareta no desu ka (Did you see the mountain?),” and• the passive voice“dareka ni mirare masita (I was seen by someone.).”We previously discussed the fact that it is hard to paraphrase passive forms by manually written rules (Ohtake and Yamamoto, 2001). That study suggested that example-based paraphrasing
is suitable for paraphrasing passive forms. Thus, in this paper, we also collect paraphrases frompassive forms to non-passive forms, and analyze them.
The definition of a passive formed sentence in this paper is as follows:a sentence that has a morpheme that is “rareru” or “reru” and a POS that is “dousisetubi (verb ending3 )” in its last predicate phrase.
Some examples of passive formed sentences are as follows: “iie, kinsi sareteimasu. (No, you
can’t.)”, “okane o nusumare masita. (My money was stolen.)”, “goyoyaku wa sarete imasuka.
(Did you make a reservation?)”.
3 Extracting Paraphrases
We extracted paraphrasing examples from a Japanese-English parallel corpus that consists oftravel conversations and that has been sentence aligned. The corpus is made for Japanese speakers traveling to English-speaking countries. The parallel corpus has 162,320 sentences. The
number of English sentences is 98,290 and the number of Japanese sentences is 102,664. This
corpus provides many instances of paraphrasing, since translations preserve the meaning of theoriginal source but may use different words to convey the meaning.
The method used for collecting paraphrases is a simple one. The method we employed isfundamentally the same as that used by Barzilay and McKeown (Barzilay and McKeown, 2001).
This method treats multiple Japanese translations corresponding to one English sentence as
instances of paraphrasing. Unlike the situation of Barzilay and McKeown, we do not have toaddress the alignment or the differences in styles, since the parallel corpus has been sentencealigned.
We employed ChaSen4 as a morphological analyzer and CaboCha 5 as a parser to obtain thetwo kinds of paraphrases.
The collecting method is as follows:3This can also be described as an auxiliary verb.
http://chasen.aist-nara.ac.jp/
5
http://cl.aist-nara.ac.jp/˜taku-ku/software/cabocha/
4382

Step 1: If there are some Japanese translations Ji1 , · · · , Jim corresponding to an English sentence
E i , then we form all combinations of the two translations Ji j and Jik , where 1 ≤ j, k ≤m, j , k, and the combined translations are paraphrases. (This operation is illustrated in
Figure 1.)
Step 2: Do Step 1 for all English sentences.

EnglishJapanese

Ei

Ji1

Japanese paraphrases
Ji1

.
.
.

.
.
.

Ji1

Jim

Jij

Ji2
.
.
.
.
.
.

Ji3
Jik

Figure 1: Extracting Paraphrases
In addition, we can gather more paraphrases by the following operations:
1. Collect English translations corresponding to a Japanese sentence in the collected paraphrases.
2. Merge the Japanese translations of the collected English sentences into the collected paraphrases.
These operations are illustrated in Figure 2.
Of course, we can do this operation recursively, but, at the same time, the gathered paraphrases may be useless. Thus, in this paper, we reject this operation. A total of 102,747 pairsof Japanese paraphrases were obtained by doing Steps 1 and 2 above. Table 1 shows someexamples of pairs for English sentence and its translated sentences of Japanese.
4 Analysis of Context Dependency
We analyzed how many extracted paraphrases are directly applicable for use in an examplebased paraphrasing method. A directly applicable example means that the paraphrase is contextindependent.
From the 102,747 pairs of collected paraphrasing examples, we extracted paraphrases fromadjectival predicate sentences to non-adjectival predicate sentences and from passive formed
sentences to non-passive formed sentences. We removed examples that had more than fiveparaphrases because they appeared likely to cause the following two problems.
First, in these cases, the differences between Pi1 , · · · , and Pim tend to be very small, suchas local changes of functional words. Therefore, these paraphrases are subsidiary examples.
Second, the paraphrasing would be very complicated if we tried to apply these cases.
383

Japanese paraphrases
Ji1
Ji1
Jij

Ji2
.
.
.
.
.
.

Ji3
Jik

Collect English
translations
Ei
.
.
.

Ex

Jx1
.
.
.

Japanese sentences
corresponding to Ex

Jxm
merge
Figure 2: Expansion of Extracting Paraphrases
Table 1: Examples of pairs for English sentence and Japanese sentences
English sentence Japanese sentences
I’d like a refund. harai modosi sitai no desuga.
henkin site kudasai.
henkin o onegai sitai no desuga.
I lost my wallet. saifu o otosite simattan desu.
saifu o nakusite simai masita.
saifu o funsitu simasita.
I have a fever.
netu ga arun desu.
netu ga takai no desu.
netuppoi desu.
hatunetu desu.

When we removed the examples that had more than five paraphrases, the average number ofparaphrases to one expression both for the adjectival predicate sentences and the passive formed
sentences was less than 2.0.
As a result of this restriction, we obtained 3,660 paraphrasing examples of adjectival predicate sentences and 485 paraphrasing examples of passive formed sentences.
Examples of paraphrases extracted from a bilingual corpus are shown in Table 2. The tableshows the original English sentence and its Japanese paraphrases by the formulation J s /J p . The
class and the feature will be introduced in this section and Section 5, respectively.
4.1 Analysis of paraphrases of adjectival predicate sentences
We analyzed the 3,660 paraphrases of adjectival predicate sentences and classified these examples into the following three classes:(A) Examples that do not depend on context and are applicable at all times.
384

Table 2: Examples of extracted paraphrases
EnglishJapanese paraphrase
class
Are you free tomorrow?
asita wa hima desuka. /asita wa hima. (A)I’d like a room with a bath.
ofurotuki no heya ga ii no desuga. /(A)basutuki no heya o onegaisimasu.
When was it built?
itu taterareta no desuka. /(A)taterata no wa itu desuka.
I miss you.
anata ni aenakute sabisii. /(A)anata ni aitai.
How can I get to this address? koko e wa dou ittara iinodesyou. /(B)kono jyuusyo ni ikitai no desuga.
That’s good.
kore wa ii. /kasikomari masita.
(C)feature(L)(L)(S)(S)(E)——(B) Examples that depend on context. In other words, there are some contexts in which applying these examples is improper.
(C) Examples that are completely improper.
The result of this classification is shown in Table 3. When we classified the examples, we did
not view each original English sentence.
Table 3: Classification for paraphrases of adjectival predicate sentences
class
(A)
(B) (C)
examples
2066 1233 361
proportion (%) 56.45 33.69 9.86
From Table 3, we can conclude that more than half of the paraphrases are directly applicableto paraphrasing. However, it is hard to automatically distinguish class-(A) from class-(B) or(C). To achieve example-based paraphrasing, we must determine how to eliminate class-(B) or(C) examples from the examples automatically extracted.
We can also conclude that almost 10% of the paraphrases extracted are included in class(C). One reason is that we extracted paraphrases without considering contextual information.
In the parallel corpus that we employed, contextual information is written with each sentence
in some way. For example, some keywords, such as “at the airport” or “shopping” express
context. However, these contextual keywords are not machine readable. We could not employ
this contextual information, therefore, and thus improper paraphrases were extracted. If wewere to employ this contextual information, we could not only automatically distinguish class(A) from class-(B) or (C), but we could also achieve paraphrasing with examples of class-(B).
In addition, some other points resulted in the class-(B) or (C) examples. One is the qualityof the translations. The target corpus consists of travel conversations for Japanese speakers
traveling to English-speaking countries. Thus, the Japanese examples tend to be unnaturalexpressions. This is because the writer who wrote the unnatural Japanese expressions is not anative Japanese speaker. These unnatural Japanese expressions are related in part to class-(B)or (C) examples.
385

A sentence :Jij

The contextual information of Jij : C(Jij)A paraphrase : Jiα

C(Jiα ) = C(Jiγ ) orC(Jiβ) = C(Jiγ )

Jiβ

Yes

class-(B) paraphrase

start

No

C(Jiα ) = C(Jiβ )Noclass-(C) paraphraseclass-(A) paraphrase

Yes

Is there Jiγ
that has strong resemblanceYes

to Jiα or JiβNounsure: class-(A) or (B) paraphrase

Figure 3: Recognizing the classes of examples
Let us consider a method of recognizing the classes of examples. The contextual information
C(Ji j ) of a sentence Ji j is given by words (e.g., at restaurant, in an airplane, and so on). Paraphrases are given in the form Jiα → Jiβ . When C(Jiα ) = C(Jiβ ) and a sentence Jlγ that has astrong resemblance to Jiα or Jiβ is given, if C(Jiα ) , C(Jlγ ) or C(Jiβ ) , C(Jlγ ), then the example
Jiα → Jiβ is included in class-(B). On the other hand, if C(Jiα ) , C(Jiβ ), then the example isincluded in class-(C). This flow is shown in Figure 3.
Applying class-(B) examples to paraphrasing requires the contextual information C(K) of thetarget sentence K. If C(K) is given properly, we can estimate whether a paraphrase K → J iαthat is applied to sentence K is applicable or not. This is because, if C(K) , C(J iα ), then theparaphrase is not applicable.
To give the context of a sentence, we can use the topic as an approximation of context (Florianand Yarowsky, 1999). This seems to be suitable for traveling conversations, because the target
domain is not very broad, and several topic detection methods are available. Fortunately, thecorpus that was we employed has such contextual information. The contextual information is
given by several topic words. However, currently available topic words are very rough andthere are many inconsistencies. Thus the methods shown in above will be very promising if we
prepare proper topic words and remove the inconsistencies.
4.2 Analysis of paraphrases of passive formed sentences
We also analyzed 485 paraphrases of passive formed sentences and classified these examples
into three classes: (A), (B), and (C). Table 4 shows the results of this classification.
Table 4: Classification for paraphrases of passive formed sentences
class
(A)
(B) (C)
examples294
182
9
proportion (%) 60.62 37.53 1.86
From Tables 3 and 4, we can conclude that the difference in the type of classified paraphrase386

has little effect on the distribution of the classes. Almost 60% of the paraphrases were directly
applicable to example-based paraphrasing.
5 Classifying Context-independent Examples
More than half of both the adjectival predicate sentences and the passive formed sentences were
context-independent. We analyzed the types of paraphrasing that such examples belonged to.
We divided the paraphrases in the context-independent paraphrasing examples into the following three types: (L) lexical paraphrasing, (S) syntactical paraphrasing, and (E) semantic
paraphrasing.
(L) Lexical paraphrasing handles differences in notation (such as differences in characters,e.g., the Chinese character vs the Japanese syllabary), or differences in verbal suffixes (suchas “X site kudasai (Please, do X)” to “X site kudasai masenka”). (S) Syntactical paraphrasing
handles the changing of word order (such as “donokuraino takasa (how tall is ...)” to “ takasaha
donokurai”), or paraphrasing predicate parts with changing particles (such as “katto dake deiidesu (Just a haircut, please)” to “katto dake o onegai simasu”). (E) Semantic paraphrasing
handles paraphrases that are not covered by (L) or (S).
We have already showed that manually written rules are practical for local changed paraphrases in (Yamamoto, 2001; Ohtake and Yamamoto, 2001). Thus, we expect that the examples classified into class-(A) include not local changed paraphrases but substantial changed
paraphrases. To clarify what types of paraphrasing are included in these examples, we assigned
the following three features to the class-(A) examples:(L) Lexical paraphrasing achieves the example. By using the changed part and its fore-andafter morphemes, the obtained paraphrases have this feature.
Examples: pasupôto o nakusite simai masita. (I have lost my passport.)→ pasupôto o funsitusite simai masita.
otearai wa doko desyouka. (Where’s the restroom?)→ toire wa doko desuka.
(S) Syntactical paraphrasing achieves the example. Changing a word order or its voice andchanging a predicate by exchanging particles obtain this feature. Some examples are as
follows, where roman letters show the original English sentence, and italic letters show adirect translation:kôhî ga hosii no desuga. (I’ll have some coffee.)→ kôhî o kudasai. (May I have coffee, please?)dono kurai fukai desuka. (How deep is it?)→ fukasa ha donokurai desuka. (How is the depth of it?)(E) Semantic paraphrasing achieves the example. In other words, examples that cannot be
obtained by combining feature-(L) and (S) paraphrasing have this feature.
Examples:sore ijyou wa yasuku nari masenka. (Is it able to go down in price more?)→ sore ga saisyuutekina nedan desuka. (Is that your last price?)doko de haraeba ii desuka. (Where should I pay?)→ siharai basyo wa doko desuka. (Where is the paying place?)387

We classified 2,066 paraphrases into the following four classes: having only (L), having only
(S), having both (L) and (S), and having only (E). The classification result is shown in Table 5.
Table 5: Classification of class-(A) examples of adjectival predicate sentencesfeature(L)(S) (L)+(S)(E)
examples
624
355
321
766
proportion (%) 30.20 17.18
15.54 37.08
Through observations of examples that have feature-(L), or self-examination, it seems easy
to extract paraphrasing rules from these paraphrases. In addition, from the examples that havefeature-(S), although we need dictionaries, such as verbal case frames, we can extract paraphrasing rules. On the other hand, from the examples that have feature-(E), it is difficult to
extract paraphrasing rules. If we collect these feature-(E) examples, broad paraphrasing is possible. Unfortunately, almost 60% of the collected examples have feature-(L) or (S). Thus, wecannot expect many paraphrasing examples having feature-(E) to be collected from the parallel
corpus we employed.
If it is difficult to collect paraphrases having feature-(E), it appears reasonable to create such
examples by hand. However, some examples having feature-(E) seem to be difficult to create
manually in a short time. Making these examples by hand, therefore, may be a difficult task.
Meanwhile, in the collected examples, some adjectival predicate sentences have examples
that have both feature-(E) and feature-(L+S) 6 at the same time. In other words, Ji j → Jik
has the feature-(E) and the other Ji j → Jil has the feature-(L+S). For example, “suwatte moii desuka.(May I sit here)→ kono seki wa aite imasuka.7 ” has the feature-(E) and “suwatte
mo ii desuka (May I sit here)→ suwatte mo kamai masenka” has the feature-(L). From theseexamples, to satisfy a paraphrasing factor, the paraphrases having feature-(L) or (S) might
adequately work for paraphrasing.
In addition, a part of feature-(E) examples is subordinate. For example, when an example
J s → J p has feature-(E), and another example J s → J 0p has also feature-(E), if J 0p can be obtainedby applying paraphrases having feature-(L+S) to J p , then these examples are subordinate. We
estimate, thus, that the number of examples having feature-(E) is essentially smaller than thenumber shown in Table 5.
We also classified 294 paraphrases of passive formed sentences. The classification result isshown in Table 6.
Table 6: Classification of class-(A) examples of passive formed sentencesfeature(L)(S) (L)+(S)(E)
examples
37
25
20
212
proportion (%) 12.59 8.50
6.80 72.11
The distribution is very different from that of adjectival predicate sentences. This difference
tells us that the type of paraphrase exerts a strong influence on the distribution of the paraphrasing types.
6
7

The feature-(L+S) means feature-(L) or (S), or both (L) and (S).
The direct translation of this Japanese expression is “Is this seat taken?”388

On the other hand, an interesting fact exists that is not shown in Table 6. The 85 passive
forms of the verb “fukumu (to include)” constituted the 212 examples that have feature-(E).
The 212 examples consist of 36 verbs. The verb “suru (to do)” has 36 examples in the second
place. Judging from these facts, it is very significan that the verb “fukumu (to include)” has 85
examples. The verb “fukumu (to include)” may be frequently used in traveling situations, suchas renting a car (e.g., Does it include insurance?), reserving a hotel room, etc. (e.g., Are any
meals included?), or shopping (e.g., Does it include tax?), and so on.
6 Related Works
To date, there has been no work directly related to analyzing paraphrases extracted from corpora. This is because the methods or techniques used to extract paraphrases from corpora havebeen premature, and few people have an interest in collecting paraphrases. However, there aresome slightly related works.
We mentioned that an example-based paraphrasing method is suitable for passive formed
sentences from the viewpoint of paraphrasing honorifics (Ohtake and Yamamoto, 2001). The
results shown in this paper support this discussion.
The first attempt to derive paraphrasing rules from corpora was undertaken by Jacquemin
et al., who investigated morphological and syntactic variants of technical terms (Jacquemin etal., 1997). Although, these rules achieve high accuracy in identifying term paraphrases, thetechniques used have not been extended to other types of paraphrasing.
Lapata investigated polysemous adjectives whose meanings vary depending on the nouns they
modify (Lapata, 2001). Lapata acquired the meanings of these adjectives from a large corpus
and proposed a probabilistic model that provides a ranking of the set of possible interpretations.
Statistical techniques were also successfully used by Lapata to identify paraphrases of adjective
phrases.
Barzilay and McKeown presented an unsupervised learning algorithm for the identificationof paraphrases from a corpus of multiple English translations of the same source text (Barzilay
and McKeown, 2001). They employed literary texts written by foreign authors. Their algorithm
produced 9,483 pairs of lexical paraphrases and 25 morpho-syntactic rules with very high accuracy. However, there was no discussion on extracted paraphrases, and therefore, what kinds ofparaphrases are extracted and whether it is difficult to manually write those paraphrases remain
undetermined.
From the viewpoint of machine translation, to paraphrase an expression into another expression that is easy to translate is closely related to designing a controlled language. Mitamura
reported on designing a controlled language (Mitamura, 1999). Although, there is a language
specific part, the discussion that touched on the points should be focused on provides helpful
information to consider for paraphrasing.
7 Conclusion
We analyzed two kinds of paraphrases automatically extracted from a bilingual parallel corpus.
These two kinds are focused since the expressions may be ambiguous in their meanings, orthey are frequently used in Japanese conversations. By paraphrasing these kinds to simpler andclearer expressions, easier processing becomes possible.
We investigated the following points:(a) How many of the paraphrasing examples that are automatically extracted from a parallel
corpus are directly applicable to paraphrasing?
389

(b) What types of paraphrasing will be achieved by the extracted examples?
To answer question (a), the results showed that almost 60% of the paraphrases extracted fromthe parallel corpus we employed were directly applicable for use in example-based paraphrasing. Moreover, without contextual information, it is hard to extract more examples that are
directly applicable to paraphrasing. On the other hand, contextual processing enables us to
use the class-(B) (contextually dependent) examples that account for almost 35% of extracted
paraphrases.
To answer question (b), the results clarified that the two types of paraphrases extracted froma bilingual corpus showed different distributions. Almost 35% of the context-independent paraphrasing examples of adjectival predicate sentences and almost 70% of the context-independent
paraphrasing examples of the passive formed sentences were desirable for example-based paraphrasing.
Although we analyzed two kinds of paraphrases, to analyze other kinds of paraphrases is left
as a future topic for study.
Acknowledgement
The research reported here was supported in part by a contract with the Telecommunications
Advancement Organization of Japan entitled, ”A study of speech dialogue translation technology based on a large corpus”.
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