A Class-oriented Approach to Building a Paraphrase Corpus

Atsushi Fujita
Graduate School of Informatics,
Kyoto University
fujita@pine.kuee.kyoto-u.ac.jp

Abstract
Towards deep analysis of compositional classes of paraphrases, we haveexamined a class-oriented framework
for collecting paraphrase examples, inwhich sentential paraphrases are collected for each paraphrase class separately by means of automatic candidate generation and manual judgement. Our preliminary experiments onbuilding a paraphrase corpus have so
far been producing promising results,
which we have evaluated according tocost-efficiency, exhaustiveness, and reliability.

1 Introduction
Paraphrases are alternative ways of conveying thesame content. The technology for paraphrase
generation and recognition has drawn the attention of an increasing number of researchers because of its potential contribution to a broad rangeof natural language applications.
Paraphrases can be viewed as monolingualtranslations. From this viewpoint, research onparaphrasing has adapted techniques fostered in
the literature of machine translation (MT), suchas transformation algorithms (Lavoie et al., 2000;
Takahashi et al., 2001), corpus-based techniques
for paraphrase pattern acquisition (Barzilay and
McKeown, 2001; Shinyama and Sekine, 2003;
Quirk et al., 2004), and fluency measurements(Lapata, 2001; Fujita et al., 2004).
One thing the paraphrasing community is stilllacking is shared collections of paraphrase ex-

Kentaro Inui
Graduate School of Information Science,
Nara Institute of Science and Technology
inui@is.naist.jp

amples that could be used to analyze problems
underlying the tasks and to evaluate the performance of systems under development. To our best
knowledge, the paraphrase corpus developed by
Dolan et al. (2004) is one of the very few collections available for free1 . Development of paraphrase corpora raises several issues: what sortsof paraphrases should be collected, where paraphrase examples can be obtained from, how thecoverage and quality of the corpus can be ensured,
how manual annotation cost can be effectively reduced, and how collected examples should be organized and annotated.
Obviously these issues should be discussedwith the purpose of each individual corpus taken
into account. In this paper, we address the issues of building a gold-standard corpus that is to
be used to evaluate paraphrase generation models
and report on our preliminary experiences taking
Japanese as a target language. Our approach ischaracterized by the following:• We define a set of paraphrase classes basedon the syntactic features of transformation
patterns.
• We separately collect paraphrase examples
for each paraphrase class that are considered
to be linguistically explainable.
• We use a paraphrase generation system toexhaustively collect candidate paraphrases
from a given text collection, which are thenmanually labeled.
15801 sentence pairs from their comparable corpus havebeen judged manually and available fromhttp://research.microsoft.com/research/nlp/msr paraphrase.htm

2 Goal

In example (1), a verb phrase, “shigeki-o uketa (to receive an inspiration),” is paraphrased into
Paraphrases exhibit a wide variety of patterns
a verbalized form of the noun, “shigeki-s-are-ta
ranging from lexical paraphrases to syntactic(to be inspired).” We can find a number of paratransformations and their combinations. Somephrases that exhibit a similar pattern of syntacticof them are highly inferential or idiomatic andtransformation in the same language and groupdo not seem easy to generate only with syntactic
such paraphrases into a single class, which isand semantic knowledge. Such groups of parapossibly labeled “paraphrasing of light-verb conphrases require us to pursue corpus-based acquisistruction.” Likewise, paraphrases exemplified bytion methods such as those described in Section 3.
(2) constitute another class, so-called transitivMore importantly, however, we can also find
ity alternation. Example (3) is of the locative
quite a few patterns of paraphrases that exhibit aalternation class and example (4) the compound
degree of regularity. Those groups of paraphrases
noun decomposition class. In example (5), a verb
have a potential to be compositionally explained“atatamaru (to be warmed)” is paraphrased intoby combining syntactic and semantic properties
its adjective form, “atatakai (be warm).” Paraof their constituent words. For instance, the folphrases involving such a lexical derivation arelowing paraphrases2 in Japanese are considered toalso in our concern.
be of these groups.
One can learn the existence of such groupsofparaphrases and the regularity each group exuke-ta.
(1) s. eiga-ni shigeki-ofilm-DAT inspiration-ACC to receive-PAST
hibits from the linguistic literature (Mel’čuk and
I received an inspiration from the film.
Polguère, 1987; Jackendoff, 1990; Kageyama,t. eiga-ni shigeki-s-are-ta.
2001). According to Jackendoff and Kageyama,film-DAT to inspire-PASS-PAST
for instance, both transitivity alternation and locaI was inspired by the film.
tive alternation can be explained in terms of the(2) s. sentakumono-ga soyokaze-ni yureru.
syntactic and semantic properties of the verb inlaundry-NOM
breeze-DAT to sway-PRES
volved, which are represented by what they call
The laundry sways in the breeze.
Lexical Conceptual Structure. The systematicity
t. soyokaze-ga sentakumono-o yurasu.
underlying such linguistic accounts is intriguing
breeze-NOM laundry-ACC
to sway-PRES
also from the engineering point of view as it could
The breeze makes the laundry sways.
enable us to take a more theoretically motivated(3) s. glass-ni mizu-o mitashi-ta.
but still practical approach to paraphrase generaglass-DAT water-ACC to fill-PASTtion.
I filled water into the glass.
Aiming at this goal leads us to consider buildt. glass-o mizu-de mitashi-ta.
ing a paraphrase corpus which enables us to evalglass-ACC water-IMP to fill-PAST
uate paraphrase generation systems and conduct
I filled the glass with water.
error analysis for each paraphrase class sepa(4) s. kare-wa kikai-sousa-ga
jouzu-da.
rately. Our paraphrase corpus should therefore be
he-TOP machine operation-NOM be good-PRES
organized according to paraphrase classes. More
He is good at machine operation.
specifically, we consider a paraphrase corpus sucht. kare-wa kikai-ojouzu-ni sousa-suru.
that:
he-TOP machine-ACC well-ADV to operate-PRES• The corpus consists of a set of subcorpora.
He operates machines well.
• Each subcorpus is a collection of paraphrase(5) s. heya-wa mou atatamat-teiru.
sentence pairs of a paraphrase class.
room-TOP already to be warmed-PERF• Paraphrases collected in a subcorpus suffiThe room has already been warmed up.
ciently reflect the distribution of the occurt. heya-wa mou atatakai.
rences in the real world.
room-TOP already be warm-PRES
The room is warm.
Given a paraphrase class and a text collection,2
the goal of building a paraphrase corpus is to colFor each example, “s” and “t” denote an original sentence and its paraphrase, respectively.
lect paraphrase examples belonging to the classas exhaustively as possible from the text collection at a minimal human labor cost. The resultant
corpus should also be reliable.

3 Related work
Previous work on building paraphrase corpus(collecting paraphrase examples) can be classified
into two directions: manual production of paraphrases and automatic paraphrase acquisition.
3.1 Manual production of paraphrases
Manual production of paraphrase examples has
been carried out in MT studies.
For example,
Shirai et al. (2001) andKinjo et al. (2003) use collections of JapaneseEnglish translation sentence pairs.
Giventranslation pairs, annotators are asked to produce
new translations for each side of the languages.
Sentences that have an identical translation
are collected as equivalents, i.e., paraphrases.
Shimohata (2004), on the other hand, takes asimpler approach in which he asks annotators to
produce paraphrases of a given set of English
sentences.
Obviously, if we simply asked human annotators to produce paraphrases of a given set of sentences, the labor cost would be expensive while
the coverage not guaranteed. Previous work,
however, has averted their eyes from evaluating
the cost-efficiency of the method and the coverage of the collected paraphrases supposedly because their primary concern was to enhance MT
systems.
3.2 Automatic paraphrase acquisition
Recently, paraphrase examples have been automatically collected as a source of acquiring paraphrase knowledge, such as pairs of synonymous
phrases and syntactic transformation templates.
Some studies exploit topically related articles
derived from multiple news sources (Barzilay andLee, 2003; Shinyama and Sekine, 2003; Quirk etal., 2004; Dolan et al., 2004). Sentence pairs thatare likely to be paraphrases are automatically collected from the parallel or comparable corpora,using such clues as overlaps of content words andnamed entities, syntactic similarity, and reference
description, such as date of the article and positions of sentences in the articles.

Automatic acquisition from parallel or comparable corpora, possibly in combination with manual correction, could be more cost-efficient thanmanual production. However, it would not ensure
coverage and quality, because sentence pairing algorithms virtually limit the range of obtainable
paraphrases and products tend to be noisy.
Nevertheless, automatic methods are useful to
discover a variety of paraphrases that need further
exploration. We hope that our approach to corpus
construction, which we present below, will work
complementary to those directions of research.

4 Proposed method
Recall that we require a corpus that reflects the
distribution of the occurrences of potential paraphrases of each class because we aim to use it for
linguistic analysis and quantitative evaluation ofparaphrase generation models.
Since the issues we address here are highly empirical, we need to empirically examine a rangeof possible methods to gain useful methodological insights. As an initial attempt, we have so far
examined a simple method which falls in the middle of the aforementioned two approaches. The
method makes use of an existing paraphrase generation system to reduce human labor cost as wellas to ensure coverage and quality:
Step 1. For a given paraphrase class, develop a
set of morpho-syntactic paraphrasing patterns and lexical resources.
Step 2. Apply the patterns to a given text collection using the paraphrasing system to generate a set of candidate paraphrases.
Step 3. Annotate each candidate paraphrase withinformation of the appropriateness according to a set of judgement criteria.
We use morpho-syntactic paraphrasing patterns
derived from paraphrase samples in an analogous
way to previous methods such as (Dras, 1999).
For instance, from example (1), we derive a paraphrasing pattern for paraphrasing of light-verb
constructions:(6) s. N -o(⇒V ) V
N -ACCV

t. V (N )V (N )

where N is a variable which matches with a noun,
V a verb, V (N ) denotes the verbalized form of

(c) second opinion(correct / incorrect)(a) source sentence

Given

Obligatory(b) automaticallygeneratedparaphrase(c) annotator’s judge(correct / incorrect)(f) free comments

Obligatory

Optional
Optional(e) confirmed (revised)paraphrase(d) error tags

Figure 1: Annotation schema.
N , and the subscripted arrow in (6s) indicates thatN -o depends on V .
To exhaustively collect paraphrase examples
from a given text collection, we should not excessively constrain paraphrasing patterns. To avoid
overly generating anomalies, on the other hand,
we make use of several lexical resources. For instance, pairs of a deverbal noun and its transitive
form are used to constrain N and V (N ) in pattern(6). This way, we combine syntactic transformation patterns with lexical constraints to specify aparaphrase class. This approach is practical given
the recent advances of shallow parsers.
For the judgement on appropriateness in Step 3,
we create a set of criteria separately for each paraphrase class. When the paraphrase class in focus
is specified, the range of potential errors in candidate generation tends to be predictable. We therefore specify judgement criteria in terms of a typology of potential errors (Fujita and Inui, 2003);
namely, we provide annotators with a set of conditions for ruling out inappropriate paraphrases.
Annotators judge each candidate paraphrasewith a view of an RDB-based annotation tool
(Figure 1). Given (a) a source sentence and(b) an automatically generated candidate paraphrase, human annotators are asked to (c) judge
the appropriateness of it and, if it is inappropriate, they are also asked to (d) classify the underlying errors into a predefined taxonomy, andmake (e) appropriate revisions (if possible) and(f) format-free comments.

5 Preliminary trials
To examine how the proposed method actuallywork regarding the issues, we conducted preliminary trials, taking two classes of Japanese paraphrases: paraphrasing of light-verb constructions
and transitivity alternation. This section describes the settings for each paraphrase class.
We sampled a collection of source sentencesfrom one year worth of newspaper articles: Nihon Keizai Shinbun3 , 2000, where the averagesentence length was 25.3 words. The reason
why we selected newspaper articles as a sample
source was that most of the publicly available
shallow parsers for Japanese were trained on atree-bank sampled from newspaper articles, and anewspaper corpus was available in a considerablylarge scale. We used for candidate generation themorphological analyzer ChaSen4 , the dependency
structure analyzer CaboCha5 , and the paraphrase
generation system K URA6 .
Two native speakers of Japanese, adults graduated from university, were employed as annotators. The process of judging each candidate paraphrase is illustrated in Figure 2. The first annotator was asked to make judgements on each candidate paraphrase. The second annotator inspected
all the candidates judged correct by the first an3

http://sub.nikkeish.co.jp/gengo/zenbun.htm
http://chasen.naist.jp/
5
http://chasen.org/˜taku/software/cabocha/
6
http://cl.naist.jp/kura/doc/
4

compiled into pairs of dependency trees with
uninstantiated components, and were applied toCorrect source sentences with the paraphrase generation
system, which carried out dependency structurebased pattern matching. 2,566 candidate paraDeferred phrases were generated from 10,000 source sentences.
In the judgement phase, the annotators were
Incorrect
also asked to revise erroneous candidates if possible. The following revision operations were alFigure 2: Judgement procedure.
lowed for LVC:
• Change of conjugations
notator. To reduce the labor cost, only a small• Change of case markers
subset of candidates that the first annotator judged• Insert adverbs
incorrect were checked by the second annotator,
leaving the rest labeled incorrect. Once in sev• Append verbal suffixes, such as voice, aseral days, the annotators discussed cases on which
pect, or mood devices
they disagreed, and if possible revised the annoWhen pattern (7) is applied to sentence (1s), fortation criteria. When the discussion did not reachinstance, we need to add a voice device, “are (pasa consensus, the judgement was deferred.
sive),” to correctly produce (1t). In example (8),on the other hand, an aspectual device, “dasu (in5.1 Paraphrasing of light-verb constructions
choative),” is appended, and a case marker, “no(LVC)(GEN),” is replaced with “o (ACC).”An example of this class is given in (1). A light- (8) s. concert-no ticket-no hanbai-o hajime-ta.
verb construction consists of a deverbal noun
concert-GEN ticket-GEN sale-ACC to start-PAST(“shigeki (inspiration)” in example (1)) governed
We started to sale tickets for concerts.
by a light-verb (“ukeru (to receive)”). A parat. concert-no ticket-o hanbai-shi-dashi-ta.
concert-GEN ticket-ACC to sell-INCHOATIVE-PASTphrase of this class is a pair of a light-verb conWe started selling tickets for concerts.
struction and its unmarked form, which consistsof the verbalized form of the deverbal noun where
So far, 1,114 candidates have been judged7 withthe light-verb is removed.
agreements on 1,067 candidates, and 591 paraLet N , V be a deverbal noun and a verb, andphrase examples have been collected.
V (N ) be the verbalized form of N . Paraphrases
5.2 Transitivity alternation (TransAlt)of this class can be represented by the following
paraphrasing pattern:
This class of paraphrases requires a collection ofpairs of intransitive and transitive verbs, such as(7) s. N -{ga, o, ni}(⇒V ) V
N -{NOM, ACC, DAT } V“yureru (to sway)” and “yurasu (to sway)” in ext. V (N )ample (2). Since there was no available resource
V (N )
of such knowledge, we newly created a miniIn the experiment, we used three more patterns to
mal set of intransitive-transitive pairs that were
gain the coverage.
required to cover all the verbs appearing in theWe then extracted 20,155 pairs of deverbal
source sentence set (25,000 sentences). We firstnoun and its verbalized form (e.g. “shigeki (inretrieved all the verbs from the source sentences
spiration)” and “shigeki-suru (to inspire)”) fromusing a set of extraction patterns implemented in
the Japanese word dictionary, IPADIC (version
the same manner as paraphrasing patterns. Ex2.6.3)3 . This set was used as a restriction on
ample (9) is one of the patterns used, where Nx
nouns that can match with N in a paraphrasing
matches with a noun, and V a verb.
pattern. On the other hand, we made no restric7
983 candidates for the first 4,500 sentences were fullytion on V , because we had no exhaustive list
judged, and 131 candidates were randomly sampled fromthe remaining portion.
of light-verbs. The patterns were automatically
1st annotator

2nd annotator

Correct

Discussion

Label

Correct

Correct

Incorrect

Candidate
Candidate

Deferredparaphrase
paraphrase

Correct

Incorrect

Incorrect

UnseenIncorrect

(9)s. N1 -ga(⇒V ) N2 -ni(⇒V ) V
N1 -NOM

N2 -DAT

Table 1: Statistics of the resultant corpora.

Vt. no change.
We then manually examined the transitivity ofeach of 800 verbs that matched with V , and collected 212 pairs of intransitive verb vi and its transitive form vt . Using them as constraints, we implemented eight paraphrasing patterns as in (10).
(10) s. N1 -ga(⇒Vi ) N2 -ni(⇒Vi ) Vi
N1 -NOM

N2 -DATVi

Paraphrase class# of source sentences# of patterns
Type of lexical resources
Size of lexical resource# of candidates# of judged candidates# of incorrect candidates# of correct candidates# of paraphrase examples
Working hours

LVC
10,0004
n, vn 
20,155
2,566
1,067
520
547
591
118

TransAlt
25,0008
vi , vt 
212
985
964
503
461
484
169.5

t. N2 -ga(⇒Vt (Vi )) N1 -o(⇒Vt (Vi )) Vt (Vi )N2 -NOM

N1 -ACC

Vt (Vi )where Vi and Vt (Vi ) are variables that match withvi and vt , respectively. By applying the patterns
to the same set of source sentences, we obtained
985 candidate paraphrases.
We created a set of criteria for judging appropriateness (an example will be given in
Section 6.4) and revision examples for the following operations allowed for this trial:• Change of conjugations• Change of case markers• Change of voices
964 candidates have gained an agreement, and484 paraphrase examples have been collected.

6 Results and discussion
Table 1 gives some statistics of the resultant paraphrase corpora. Figures 3 and 4 show the numberof candidate paraphrases, where the horizontal
axes denote the total working hours of two annotators, and the vertical axes the number of candidate paraphrases. The numbers of judged, correct,
incorrect, and deferred candidates are shown.
6.1 Efficiency
2,031 candidate paraphrases have so far been
judged in total and 1,075 paraphrase examples
have been collected in 287.5 hours. The judgement was performed at a constant pace: 7.1 candidates (3.7 examples) in one hour. It is hard tocompare these results with other work because
no previous study quantitatively evaluate the efficiency in terms of manual annotation cost. However, we feel that the results have so far been satisfiable.
For each candidate paraphrase judged incorrect, the annotators were asked to classify the underlying errors into the fixed error types ((d) in

Figure 1). This error classification consumed extra time because it required linguistic expertise
which the annotators were not familiar with.
TransAlt was 1.75 times more time-consuming
than LVC because the definition of TransAlt involved several delicate issues, which made thejudgement process complicated. We return to thisissue in Section 6.4.
6.2 ExhaustivenessTo estimate how exhaustively the proposed
method collected paraphrase examples, we randomly sampled 750 sentences from the 4,500
sentences that were used in the trial for LVC,
and manually checked whether the LVC paraphrasing could apply to each of them. As a result, 206 examples were obtained, 158 of whichwere those already collected by the proposed
method. Thus, the estimated exhaustiveness was
77% (158 / 206). Our manual investigation into
the missed examples has revealed that 47 misses
could have been automatically generated by enhancing paraphrasing patterns and dictionaries,
while only one example was missed due to an error in shallow parsing. 34 cases of the 48 misses
could have been collected by adding a couple ofparaphrasing patterns. For example, pattern (11)verbalizes a noun followed by a nominalizing suffix, “ka (-ize),” as in (12).
(11) s. N -ka-{ga, o, ni}(⇒V ) V
N -ize-{NOM, ACC, DAT } V

t. V (N -ka)
V (N -ize)(12) s. kore-wa kin’yu-shijo-no

kassei-ka-ni

this-TOP financial market-GEN activation-DAT

muke-takisei-kanwa-saku-da.

to address-PAST deregulation plan-COP
This is a deregulation plan aiming at theactivation of financial market.

1200

JudgedCorrect
Incorrect
Deferred800# of judged candidates

1000# of judged candidates

1000

JudgedCorrect
Incorrect
Deferred

800

600

400

600

400

200

200

0

0
0

20

40

60

80

100

120

working hours

Figure 3: # of judged candidates (LVC).
t. kore-wa kin’yu-shijo-o
this-TOP financial market-ACC

kassei-ka-suru kisei-kanwa-saku-da.
to activate-PRES deregulation plan-COP
This is a deregulation plan which activates
financial market.

We cannot know if we have adequate paraphrasing patterns and resources before trials.
Therefore, manual examination is necessary to refine them to bridge gap between the range of paraphrases that can be automatically generated andthose of the specific class we consider.
6.3 Reliability
Ideally, more annotators should be employed toensure the reliability of the products, which, however, leads to a matter of balancing the trade-off.
Instead, we specified the detailed judgement criteria for each paraphrase class, and asked the annotators to reconsider marginal cases several days
later and to make a discussion when judgements
disagreed. The agreement ratio for correct candidates between two annotators increased as they
became used to the task. In the trial for LVC,
for example, the agreement ratio for each daychanged from 74% (day 3) to 77% (day 6), 88%(day 9), and 93% (day 11). This indicates that thejudgement criteria were effectively refined basedon the feedback from inter-annotator discussions
on marginal and disagreed cases. To evaluate thereliability of our judgement procedure more precisely, we are planing to employ the third annotator who will be asked to judge all the cases independently of the others.

0

20

40

60

80

100

120

140

160

180

working hours

Figure 4: # of judged candidates (TransAlt).
6.4 How we define paraphrase classes
One of the motivations behind our class-based approach is an expectation that specifying the target
classes of paraphrases would simplify the awkward problem of defining the boundary between
paraphrases an non-paraphrases. Our trials for thetwo paraphrase classes, however, have revealed
that it can still be difficult to create a clear criterion for judgement even when the paraphraseclass in focus is specified.
As one of the criteria for TransAlt, we tested
the agentivity of the nominative case of intransitive verbs. The test used an adverb, “muzukara(by itself),” and classified a candidate paraphrase
as incorrect if the adverb could be inserted immediately before the intransitive verb. For example, we considered example (13) as a correct
paraphrase of the TransAlt class whereas (14) incorrect because the agentivity exhibited by (14s)
did not remain in (14t).
(13) s. kare-ga soup-o atatame-ta.
he-NOM soup-ACC to warm up-PASTHe warmed the soup up.

t. soup-ga atatamat-ta. (correct)
soup-NOM to be warmed up-PASTThe soup was warmed up (by somebody).

(14) s. kare-ga koori-o tokashi-ta.
he-NOM ice-ACC to melt (vt)-PASTHe melted the ice.

t. koori-ga toke-ta. (incorrect)
ice-NOM to melt (vi)-PASTThe ice melted (by itself).

However, one might regard both paraphrases
incorrect because the information given by thenominative argument of the source sentence isdropped in the target in both cases. Thus, theproblem still remains. Nevertheless, our approach
will provide us with a considerable amounts ofconcrete data, which we hope will lead us to better understanding of the issue.

7 Conclusion
Towards deep analysis of compositional classes ofparaphrases, we have examined a class-oriented
framework for collecting paraphrase examples,in which sentential paraphrases are collected foreach paraphrase class separately by means of automatic candidate generation and manual judgement. Our preliminary experiments on building
a paraphrase corpus have so far been producing
promising results, which we have evaluated according to cost-efficiency, exhaustiveness, and reliability. The resultant corpus and resources will
be available for free shortly. Our next step is directed to targeting a wider range of paraphraseclasses.

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