Sharing User Dictionaries Across Multiple Systems
with UTX-S
AAMT Sharing/Standardization Working Group,Francis Bond,1 Seiji Okura,2 Yuji Yamamoto,3 Toshiki Murata,4Kiyotaka Uchimoto,1 Michael Kato,5 Miwako Shimazu,6 Tsugiyoshi Suzuki7
National Institute of Information and Communications Technology,
Fujitsu Laboratories Ltd., 3 CosmosHouse, 4 Oki Electric Industry Co., Ltd.,
5 Learning Consultant, 6 Toshiba Solutions Corporation, 7 Cross Language Inc.
1

2

ABSTRACTACM Classification Keywords

Careful tuning of user-created dictionaries can be very advantageous when using a machine translation system for computer aided translation. However, there is no widely used
standard for user dictionaries in the Japanese/English machine translation market. To address this issue, AAMT (theAsia-Pacific Association for Machine Translation: http://
www.aamt.info/) has established a specification of sharable
dictionaries (UTX-S: Universal Terminology eXchange —Simple), which can be used across different machine translation systems, thus increasing the interoperability of language
resources. UTX-S is simpler than existing specifications
such as UPF and TBX. It was explicitly designed to make iteasy to (a) create new user dictionaries and (b) share existinguser dictionaries. This facilitates rapid user dictionary production and avoids vendor tie in. In this study we describe
the UTX-Simple (UTX-S) format, and show that it can beconverted to the user dictionary formats for five commercial English-Japanese MT systems. We then present a casestudy where we (a) convert an on-line glossary to UTX-S,
and (b) produce user dictionaries for five different systems,
and then exchange them. The results show that the simplified
format of UTX-S can be used to rapidly build dictionaries.
Further, we confirm that customized user dictionaries are effective across systems, although with a slight loss in quality:
on average, user dictionaries improved the translations for
44.8% of translations with the systems they were built forand 37.3% of translations for different systems. In ongoing
work, AAMT is using UTX-S as the format in building up
a user community for producing, sharing, and accumulatinguser dictionaries in a sustainable way.

I.2.7 [Artificial Intelligence] Natural Language Processing—
Machine translation, I.2.m [Artificial Intelligence] Miscellaneous

Author Keywords

Interoperability of Language Resources, Machine Translation, User Dictionaries

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IWIC’09, February 20{21, 2009, Palo Alto, California, USA.
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INTRODUCTION

User and specialist domain dictionary compilation is important for machine translation, because adding terms into auser dictionary decreases failures in syntax analysis [5] aswell as improving the accuracy of translation. Yoshimura etal. [17] using 41 hierarchically organized domains (which
they call Subject Areas) marked on 9,000 words, were able
to improve the translations of 12% of the badly translated
46
nouns ( 383). Similarly, Lange and Yang [10] used 77 Domains (TERMinology CATegories) and 30 Topical Glossaries.
In a first pass through they chose two appropriate domains(the domains with the greatest number of domain-taggedwords). With these domains set, there were changes in 0–
40% of translations, and the majority of the changes were
improvements.
Another measure of how valuable user dictionaries are is thelengths people are prepared to go to make them work withexisting systems. Inaba et al. [6] develop a ”Specialized
dictionary” that is applied to an MT system accessed only
through a server. Words in the specialized dictionary are replaced by special words (e.g. “REF1”) and their translationis then replaced by the translation of the specialized wordfrom the dictionary. This allows the use of external user dictionaries, even when the MT system is a black box. Itagaki
and Aikawa [8] use a similar technique to replace words in
an SMT system — in their case translating the source wordin various contexts, finding how the system translates it andstoring that in a preprocessing step. Then in actual translation, they replace the system’s translation of the source wordwith the translation in the user dictionary. In both thesecases considerable effort has been expended in retrofittinguser dictionaries to existing MT systems.
However, user dictionary compilation is a very time-consuming
process for an individual user, and its effect may not immediately be clear for relatively small translation projects. In
addition, the formats of individuals’ dictionaries are variedif they do not use the same machine translation software.
Therefore, sharing these dictionaries can be difficult.
There are already several well designed formats for user dictionaries/terminology: TermBase eXchange: TBX1 ; the Open
Lexicon Interchange Format: OLIF2 [11] and the Universal
PlatForm: UPF3 [9]. However, to the best of our knowledge,
there are not many dictionaries available formatted as either TBX, OLIF or UPF, although all are supported by somecommercial machine translation systems.
More recently the general Lexical Markup Framework (LMF)
has also been proposed [4] as a framework for representing
dictionaries. However, LMF is a framework, it has to be specialized further in actual use, and shares the complexity of
the above systems.
All these specifications are intended to be used by professional terminologists. Their complex nature gives them theability to represent the various kinds of information needed
in a machine translation system. However, this makes themhard to use for average MT users, and none of them arewidely used in Japan. These concerns have been noted before, leading to a simplified format, TBX basic [12], whichis considerably streamlined, but it still requires a great dealof information to be entered beyond simple translation equivalents. The majority of MT users in Japan still use simple
text formats or spreadsheet lists. In order to address these
problems, we have established a new standard called UTXSimple (UTX-S). It is not meant to replace any of the existing standards for heavy machine translation users, but isinstead meant to supplement them as a lightweight variant.
As far as possible, we are making the standard compatible
with the more detailed standards.
In this paper, we introduce UTX-S. We then use UTX-S in
two experiments: one on reusing domain dictionaries andone on sharing user dictionaries. Finally we discuss futurework.
UTX-SIMPLE

The features of UTX-S are as follows:
1. Dictionary for the user - simple and easy to use: A
complicated specification merely increases users’ burdens,
and it will be forgotten eventually. A specification mustbe simple and practical to reflect and include actual MT
users’ needs, viewpoints, and scenarios.
2. Entry as a technical term: UTX clearly defines the domain of a dictionary, and adheres to the principle of one
word, one meaning. Because the format does not allow
one to enter information to choose between translations,a given term is restricted to a single translation. Therefore, the domain should be restricted enough that an entry
should be a unique term within an applicable domain.
1 http://www.lisa.org/Term-Base-eXchange.32.0.html[16]
2 http://www.olif.net/index.htm
3 http://www.aamt.info/japanese/upf.htm

3. Improvement in translation accuracy: When UTX dictionaries become widespread, sharing dictionaries can be
drastically simplified. Users can compile user dictionaries
more efficiently by exchanging existing data, and UTX
will eventually contribute to improvement in translation
accuracy.
4. Multilingual and monolingual dictionary: UTX is designed for dictionaries not only between two languages
but also among multiple languages. In addition, UTX
also includes a specification for a monolingual dictionary,
which can be used for proofreading tools for terminological standardization, etc.
5. Promotion of localization of software: Especially in open
source localization projects, translation is carried out individually, and terminological standardization can be difficult. By exchanging and sharing translation resource systematically through the use of UTX, more and more common dictionaries become available, and they increase theefficiency of translation exponentially.
As UTX-Simple does not retain detailed information about
its creator, the timestamp for each entry, etc., it is not suitable for a compilation of a permanent, versatile dictionary.
However, it is practical and easy to create, use, and share. It
may be used by a variety of users regardless of their background knowledge in advanced linguistics.
We are currently establishing guidelines for notation, i.e.
how to describe each entry, for each target language. As
far as possible we will try to stay compatible with existing
guidelines, such as those for TBX, OLIF and JMDict [2].
The converter which maps between UTX-S and MT systemformats also acts as a validator. For example, if one or moremandatory properties are missing, a warning is issued according to the corresponding option of the tool. To copewith human errors, the tool will skip illformed lines during
conversion. However, by making more detailed guidelines,
and also a format defined well enough to be validated, we
hope to make it easier for people to make their existing lexical resources useful for other people.
UTX-S SpecificationsA UTX-S dictionary file is a plain text file, in UTF-8 encoding. It consists of a header (the first two lines) and the
body (all subsequent lines). We give an example in Table 1. The full specifications for UTX-S are online at http://www.aamt.info/english/utx/.
The first header line consists of the following fields, separated by semicolons:• An initial #• The string UTX-S and the version number (currently 0.92)• The source language (expressed as ISO 639-1 language
and ISO 3166-1 alpha-2 country codes)• The target language (same as source)• The date produced• Misc (normally creator and copyright info)

English Entry• Use the singular form by default
The second header line consists column headers, separated
by tabs, the first three columns are mandatory.

• Use lower case letters• An initial #• Do not use full-width letters• Do not add articles (a, an, the) or infinitival to.

• src: the source language word
• tgt: the target language word
• src:pos: the source part of speech{noun |verb |adjective |adverb |properNoun |sentence}• zero or more user defined columns, e.g.:– tgt:pos– src:plural– comment
Comment lines (defined as any line starting with a hash #)can be freely interspersed with the dictionary entries. Thismeans that it is easy to delete entries (just comment themout).
Note that the 4th and subsequent fields are all optional: inthe experiments described below we only used the first threemandatory fields. The three mandatory fields are the same
as for TBX-basic, and almost the same as those describedin use by Dillinger [3] for user dictionaries built for Logos(source term, source gender, source part of speech and targetterm). Gender is not grammaticalized for either Japaneseor English, the two languages we are most interested in, so
we do not make it a mandatory field. It could be added, forexample as src:gender.
UTX Entry Guidelines

In this system we give the guidelines for writing individual
entries.
• Add only one translation for each word-pos pair.
• Avoid words already in the system dictionary.
Part of Speech• Choose one from the following:– noun– verb– adjective– adverb– properNoun– sentence• If the POS is unknown then leave it blank: ””.
The “sentence” part of speech is for complete utterances thatshould be translated as is, with no further processing. It is
not meant to replace a full translation memory, but rather ismade available for things like greetings and proverbs, which
are rarely compositional in translation, and often hard toparse. This part-of-speech is supported by most of the commercial MT systems we tested.

Japanese Entry• Add する -suru to nominal verbs when translated as averb. E.g.,
翻訳translation noun
翻訳する translateverb• Add な -na to nominal adjectives• noun-no combinations can be treated as adjectives
e.g.: orthographic 綴りの adjective• Use either a middle dot or a space to separate foreign compounds. E.g.,
ビタビ・アルゴリズム Viterbi algorithm noun
EXPERIMENTSUTX is designed to make it simple to (a) create new user
dictionaries and (b) share existing user dictionaries.
In order to test the applicability of the UTX-S guidelines,
we performed two experiments. In the first we converted an
existing specialist dictionary to UTX-S, and in the second
we compared user dictionaries across different systems.
Test Data

For both experiments we tested by translating a small sample document. We used the OLIF Guidelines for Formulating Canonical Forms, a 147 sentence English document fromthe on-line documentation for OLIF,4 and translated it into
Japanese. The text was extracted and sentence aligned manually — the input to the systems was clean, sentence separated text. We give an example, with a manually constructedreference translation and four machine translation outputs in
Figure 1. We consider this to be text in the domain of natural
language processing.
Testing was done by (a) regression testing, as this has beenshown to be consistent and fast [14] and with (b) BLEU
(with up to 4 n-grams) [13]. The baseline was the input texttranslated by the MT system with no user dictionary.
For the regression test, the results of using different dictionaries were compared to the original translation using only
the system dictionaries. Translations were presented to an
evaluator in random order, so that the evaluator did not know
which system was which. Identical translations were scoredautomatically. Changed translations were evaluated as either
equivalent quality (0), improved (+1) or degraded (-1). The
total change was calculated by summing the scores.
All judgments were made by only one evaluator, with a different evaluator for each system (for a total of five different people). Because of the known reliability of regression
4 http://www.olif.net/documentation.htm

#UTX-S 0.92; en-US/ja-JP; 2008-03-15T10:00:00Z+09:00; copyright: AAMT, license: CC-by 3.0#src
tgt
src:pos
src:plural src:3sp src:past src:pastp src:presp src:compnew
新規のadjective
newer
fast
高速な
adjective
faster# prosody should be uncountableprosody
韻律
noun
prosodiessave
保存する verb
saves
saved
saved
savinggood evening 今晩は
sentence

src:super
newestfastestTable 1. Example of UTX-S• OLIF Guidelines for Formulating Canonical Forms(a) OLIF・正規化形式への定型化の指針“OLIF Guidelines for Formulating Canonical Forms”(b) 教会法に基づく形式を定式化するためのOLIFガイドライ ン“OLIF Guidelines for formularizing forms based on Canon Law”(c) 教会法に基づくフォームを定式化するためのOLIFガイドライ ン“OLIF Guidelines for formularizing forms based on Canon Law”(d) 正規化形式を形式化するためのOLIFガイドライン“OLIF Guidelines for formulating canonical forms”(e) 基準形を定型化するためのOLIFガイドライン“OLIF Guidelines for formulating regular forms”(reference)(MT)(MT+lingdic)(MT+user)(MT+other)

Figure 1. Example Translation with different User Dictionaries
testing, and the small scale of the project, we did not use
multiple evaluators on the same data.

• PC-Transer 2008 Professional (Cross Language Inc.)(www.crosslanguage.co.jp/products/studio2008/)

BLEU testing was done with a single reference translation,
prepared by a professional translator with expert domain knowledge. In BLEU the system translation is compared to thereference (human translation) by measuring the number ofn-grams which overlap between the two translations. We
used multibleu.perl comparing up to 4-grams.

• The HON-YAKU 2008 Premium(pf.toshiba-sol.co.jp/prod/hon_yaku/
premium/index_j.htm)

For example, consider sentence (b) OLIF Guidelines for formularizing forms based on Canon Law compared to the human reference translation (a) OLIF Guidelines for Formulating Canonical Forms. There are three 1-grams that overlap (OLIF, Guidelines, for), two 2-grams (OLIF Guidelines,
Guidelines for) and one 3-gram (OLIF Guidelines for).
The test set is small (147 sentences), but the automatic evaluation showed the same trends as the human evaluation.

Results are given for the five systems anonymized as A, B,
C, D and E.
Converting an Existing Lexicon

In the first experiment, we wanted to test how useful an
off the shelf domain dictionary was as a user dictionary.
We made a user dictionary based on lingdic. lingdic is asmall open source dictionary of Japanese-to-English computational linguistic terms.5 It is maintained by a single volunteer, enhanced with contributions from the community. It
is formatted based on the format used by EDICT, a large
collaborative Japanese-English dictionary [1].

Systems

Five commercial MT systems were tested:• LogoVista PRO 2008 Super Pack
(www.logovista.co.jp/product/
honyaku_pro2008/pro2008_st.html)• Translation Software ATLAS(www.fujitsu.com/global/services/
software/translation/atlas/lineup/)• Collaborative Translation Environment: Yakushite.Net(yakushite.net/: [15])The version we used in our experiment has 3,527 Japanesehead words and 4,123 Japanese-English pairs. We wrote an
edict2utx-s converter (including a mapping from the edict
POS set to the UTX-S set), and enhanced the dictionary in
the following ways:• Added more part-of-speech information, in particular, tags
for all verbal-nouns and nominal-adjectives.
5 www2.nict.go.jp/x/x161/en/member/bond/data/
lingdic

• Added a tag (xmt) to mark pairs which should not be used
in a machine translation dictionary.
• Produced a reverser — a program to recast the dictionary
as an English-to-Japanese dictionary.
The English - Japanese dictionary is made by reversing eachentry and sorting by the following criteria, cascaded.
1. Prefer similar translations, more specifically• prefer acronym↔acronym, long form↔long form
HPSG
HPSG
head-driven phrase structure grammar
head-driven phrase structure grammar

ＨＰＳＧ
主辞駆動句構造文法
主辞駆動句構造文法
ＨＰＳＧ

↑
↓
↑
↓

2. Prefer the translation with the highest monolingual frequency(frequencies were obtained using the Yahoo Japan Developer API).
bottom-up ボトムアップ
1,020,000 ↑bottom-up ボトムアップ法
468,000 ↓One of the testers also added some entries that we would
expect to be specific to this text, for example hwrit- as a stem
for write, writeの語幹のwrit-, nouni.
The results for adding this customized user dictionary to
lingdic and translating are given as +user in Table 2.
For the final experiment, we tested the hypothesis that a user
dictionary built for one system will also be useful in a different system. If this were not the case, then there would
be no motivation to make an interoperable user dictionary
format. To test this we simply exchanged user dictionaries:
System A uses the dictionary created for system E, B uses
the one for A, C uses the one for B and so on. The results
for using this combined user dictionary are given as +otherin Table 2.
Finally, the translator who made the reference translation
made a user dictionary by merging all five dictionaries, andadding a few more entries. We used this for one final experiment to see just how good we could get using only user
dictionaries.

3. Prefer the shorter translation
4. Sort in unicode order (to make the sort deterministic)
Note that the target language frequency is rarely the same,
so 3. and 4. are almost unused.
Some example entries for the converted, reversed lingdic are
given in the appendix, in Table 3. In our first experiment,
this dictionary was converted from UTX-S to the native formats of the five MT systems, and used as a user dictionary.
The results for this configuration are given as +lingdic in
Table 2.
Creating and Exchanging User Dictionaries

In our second experiment, five members of the AAMT working group (one for each system) translated the document
once, and then created a user dictionary designed to fix someof the problems in the MT results (to be combined withlingdic). This is not a blind test, in that the users see themachine translation output. However, this is how users typically create their own user dictionaries. Each person spenta small amount of time preparing the user dictionary (generally a couple of hours). The number of words added ranged
from 17–156, as shown in Table 2.
There was a large amount of variance in which words wereadded. Only one word was added to all five user dictionaries:
compound which was entered as hcompound, 複合語, nouni
fukugougo “compound expression” in four user dictionaries
and hcompound, 複合名 詞, -i fukugou-meishi “compoundnoun” in one. There were also many places where different
lexical choices were made: for example string was translated as hstring, 文字列, nouni mojiretsu “character array”
by three users and hstring, ストリング, nouni sutoringu“string” by two users. Either is acceptable, although use
should be consistent within a document.

RESULTS AND DISCUSSION

The results of the three experiments are shown in Table 2.
The result of the first experiment, adding a reversed existing
specialist dictionary (+lingdic), was negative. The dictionary had a negative effect on most of the systems, and only avery small positive effect on one. On average, 6.5% of sentences had their translation degraded, and the BLEU scorewent down by 0.6. There were two main reasons for the
degradation: the first was that most of the systems (all butE) had an over-strong preference for words in the user dictionary — they were preferred even over existing multiword
expressions. An example of an error caused by this was “upper case” going from 大文字 oomoji “capital letters” to 上の格 ue-no-kaku “upper (grammatical) case”. lingdic had
an entry for 格 kaku (grammatical) “case” (which was also
used correctly in the document), but it caused a degradation
for this term. This was corrected in the hand-built user dictionaries by adding an entry for hupper case, 大文字, nouni
and even for the larger noun phrase hupper and lower case,
大文字と小文字, nouni.
The second reason was that the best word was often not chosen when the dictionary was reversed. For example word
was translated as 用語 yougo “term, word” rather than 単語
tango “word”. 用語 is more common, but is a less goodtranslation for word in the linguistic domain. Instead ofmonolingual frequencies, we really need the frequencies conditioned on the source word, although these are can only befound from bilingual corpora. An alternative would be to try
and collect monolingual target language documents from thedomain in question (computational linguistics/NLP) and use
them to calculate the frequency, rather than the more general Web as corpus. Deleting only a few entries made anenormous difference, although which words were problematic varied from system to system.

System
A
B
C
D
E
Ave

Dic
Size
156
59
27
24
17
56.6

Percent Change+ling +user +other
-1.4
66.0
38.1
-19.7
56.0
20.4
-5.4
27.2
36.7
-8.2
45.6
32.7
2.0
46.9
58.5
-6.5
44.8
37.3

BLEU Score
System +ling +user
13.8 13.2
21.4
15.4 15.8
18.6
15.5 14.7
17.6
17.2 15.3
20.3
12.2 11.7
16.5
14.8 14.2
18.9+other
18.2
21.1
17.0
17.8
16.4
18.1

+ling: results with lingdic-EJ+user: results with a user dictionary built for that system+other: results with an exchanged user dictionary: A uses E, B uses A, C uses B and so on.

Table 2. Translation Results
Dillinger [3] also found that converting domain dictionaries did not always produce a useful dictionary for MT (inparticular many entries in a specialist dictionary were neveractually encountered in text). However, people found thedictionary useful when building their own user dictionaries,
and UTX-S makes it was easy to add new entries and delete
existing ones by commenting them out. One of the goals of
the AAMT working group is to try not only to convert existing dictionaries to UTX format, but also to validate themin use and get feedback. In this case, by adding the xmt
tag (don’t use for MT) to the lingdic source, we were able
to make the human use dictionary more useful for machinetranslation.

how useful user dictionaries can be. However, this dictionary
has 146 entries, almost one per translated sentence, so it isbeyond what would normally be feasible.

The results of creating a user dictionary tuned to the text
showed, as expected, that it improved the translation quality(+user). Translations improved for 44.8% of sentences according to the regression testing and the BLEU score went
up by 4.1. The BLEU score almost certainly under counts
the improvement. Because there is only one reference translation, legitimate variations such as those for string, where
the native Japanese 文字列 or the loan word ス トリング
are pretty much interchangeable, are penalized.

Having confirmed the ease-of-use of UTX-S and the efficacy
of cross-system user dictionaries, we now to turn to investigating methods of encouraging people to make and share
their dictionaries.

We were not able to quantify the ease-of-use of the format,
but qualitatively all the testers found it easy to use. In particular, people liked being able to edit the dictionaries in theeditor/spreadsheet of their choice. It was definitely easier toshare than proprietary dictionary formats.
In the next experiment, we found that user dictionaries, even
if developed for other systems, improved the translation quality (+other). As far as we know this has been assumed before, but never directly shown. Translations improved for
37.3% of sentences and the BLEU score went up by 3.3 compared to using no dictionary.
Translations for all four configurations, plus the referencetranslation, are shown in Figure 1, with glosses. The best
automatic translation is (d) with the system-specific user dictionary, but (e) with the dictionary built for another system
is also perfectly understandable.
Finally, we calculated the BLEU score for the translation
with the merged, corrected dictionary (using system D). It
was 44.52, an improvement of 27.3 points. This shows justDictionary Conversion Tools

We have created a perl script that converts from UTX-S to
the user dictionary formats of the five systems tested here.
The conversion back from user dictionary to UTX-S would
be lossy: UTX-S is less informative than the full user dictionary formats. The script is available from the UTX page(http://www.aamt.info/english/utx/).
FUTURE WORK

Our basic plan is to produce or encourage the productionof domain specific dictionaries, such as education, sport, IT,
medicine etc. These dictionaries would be tested with MT
systems, removing (or marking) entries that may be usefulfor humans, but which degrade translation quality. We will
not work on a general lexicon, as all MT systems come with
their own system dictionaries.
In open source localization projects, translation is carried out
individually, and dictionaries are not shared as they should
be. Dictionaries are scattered across various providers, andtheir licenses and formats are also varied. If these scatteredlanguage resources are centralized, the localization between
different languages can be significantly accelerated.
In order to spread UTX dictionaries in which anyone canparticipate in creation, and in order to realize open dictionaries for everyone, a shared dictionary community shouldbe established. We are considering two types of dictionary
communities for producing, sharing, and accumulating dictionaries. They can be distributed using a common framework.
• The official dictionary community (managed by AAMT
or its delegate) will offer validated dictionaries with guaranteed quality for a fee.
• The open dictionary community offers free dictionaries

with open source license and promotes mutual exchange.
AAMT or its delegate provides hosting service only, but
no management or guarantee.

Intercultural Collaboration (IWIC2007) (2007),
vol. 4568 of Lecture Notes in Computer Science, Vol.
4568, Springer, pp. 118–132.

In order to meet various needs, official dictionaries and free
dictionaries should be distinguished. Free dictionaries canbe used for no fee, although the correctness of the contents
is not guaranteed.

7. I SHIDA , T. Language grid: An infrastructure forintercultural collaboration. In IEEE/IPSJ Symposium
on Applications and the Internet (SAINT-06) (2006),
pp. 96–100. (keynote address).

Collaborations with other parties has already begun for UTX,
including cooperation with Oki’s community-oriented machine translation site Yakushite-net [15] in which users can
add their own technical terms. We have also fed back improvements to lingdic and the JMDict project and are cooperating with other projects which connect many dictionaries
and systems (such as the Language Grid [7]). While providing UTX to other parties and verifying its effectiveness, weare also interested in collaborating in terms of tool development.
CONCLUSIONS

We have presented a case study where we use the UTX-S
format to (a) convert an on-line glossary to UTX-S, and (b)produce user dictionaries for five different systems, and thenexchange them. The results showed that the simplified format of UTX-S can be used to rapidly build dictionaries. Further, we confirmed that customized user dictionaries are effective across systems, although with a slight loss in quality:
on average, user dictionaries improved the translations for
44.8% of translations with the systems they were built forand 37.3% of translations for different systems.
REFERENCES

1. B REEN , J. W. Building an electronic Japanese-English
dictionary. Japanese Studies Association of Australia
Conference (http://www.csse.monash.edu.au/~jwb/jsaa_paper/hpaper.html), 1995.
2. B REEN , J. W. JMDict: a Japanese-multilingual
dictionary. In Coling 2004 Workshop on Multilingual
Linguistic Resources (Geneva, 2004), pp. 71–78.

8. I TAGAKI , M., AND A IKAWA , T. Post-MT termswapper: Supplementing a statistical machinetranslation system with a user dictionary. In
Proceedings of the Sixth International Language
Resources and Evaluation (LREC’08) (Marrakech,
Morocco, 2008), E. L. R. A. (ELRA), Ed.
9. K AMEI , S., I TOH , E., F UJII , M., H IRAI , T., S AITOH ,Y., TAKAHASHI , M., H IYAMA , T., AND M URAKI , K.
Sharable formats and their supporting environments forexchanging user dictionaries among different MT
systems as a part of AAMT activities. In MT Summit VI(1997).
10. L ANGE , E. D., AND YANG , J. Automatic domain
recognition for machine translation. In MachineTranslation Summit VII (Singapore, 1999),
pp. 641–645.
11. L IESKE , C., M C C ORMICK , S., AND T HURMAIR , G.
The open lexicon interchange format (OLIF) comes ofage. In MT Summit VIII (Santiago de Compostela,2001), pp. 211–216.
12. M ELBY, A. K. TBX-Basic: Translation-oriented
terminology made simple. Revista Tradumàtica (inPress) (2009). (ISSN 1578-7559).
13. PAPINENI , K., ROUKOS , S., WARD , T., AND Z HU ,W. J. BLEU: a method for automatic evaluation ofmachine translation. In 40th Annual Meeting of the
Association for Computational Linguistics: ACL-2002(2002), pp. 311–318.

3. D ILLINGER , M. Dictionary development workflow for
MT: Design and management. In MT Summit VIII
(Santiago de Compostela, 2001), pp. 83–88.

14. P INKHAM , J., AND S METS , M. Machine translation
without a bilingual dictionary. In Ninth International
Conference on Theoretical and Methodological Issues
in Machine Translation: TMI-2002 (Keihanna, Japan,
2002), pp. 146–156.

4. F RANCOPOULO , G., G EORGE , M., C ALZOLARI , N.,
M ONACHINI , M., B EL , N., P ET, M., AND S ORIA , C.
Lexical markup framework (LMF). In Proceedings of
the 5th International Conference on Language
Resources and Evaluation (LREC 2006) (Genoa, Italy,
May 2006).

15. S UKEHIRO , T., K ITAMURA , M., AND M URATA , T.
Collaborative translation environment ‘Yakushite.Net’.
In Proceedings of the Sixth Natural Language
Processing Pacific Rim Symposium: NLPRS-2001(Tokyo, 2001), pp. 769–770.

5. F UJI , M. Experiments to evaluate the reading
comprehension of English-to-Japanesemachine-translated texts. In 2nd Annual Meeting of the
Association for Natural Language Processing (Tokyo,
1996), pp. 21–24.
6. I NABA , R., M URAKAMI , Y., NADAMOTO , A., AND
I SHIDA , T. Multilingual communication support using
the language grid. In International Workshop on

16. T HURMAIR , G. Exchange formats: TBX, OLIF andbeyond. LDV-Forum 21, 1 (2006), 45–56.
17. YOSHIMURA , Y., K INOSHITA , S., AND S HIMAZU , M.
Processing of proper nouns and use of estimatedsubject area for web page translation. In SeventhInternational Conference on Theoretical andMethodological Issues in Machine Translation: TMI-97(Santa Fe, 1997), pp. 10–18.

APPENDIX
EXAMPLE DICTIONARIES

Some entries from lingdic with the word lexicon or dictionary are shown in Table 3. The user dictionary created forsystem D is shown in Table 4.
#UTX-S 0.92; en-US/ja-JP; 2008-05-21; copyright: Francis Bond (2008); license: CC-by 3.0#src
tgt
src:pos
Japan Electronic Dictionary Research Institute 日本電子化辞書研究所
Japanese-to-English transfer dictionary
日英対照辞書basic lexicon
基本語彙
co-occurrence dictionary
共起辞書
collocation dictionary
共起辞書
concept dictionary
概念辞書
dictionary
辞書
noun
dictionary form
終止形
noun
generative lexicon
生成的辞書
idiom dictionary
慣用語句辞書system dictionary
システム辞書terminology dictionary
専門辞書
user dictionary
利用者辞書
word collocation dictionary
単語共起辞書Table 3. Sample Entries from lingdic.enja.utx#UTX-S 0.92; en-US/ja-JP; 2008-05-29; copyright: AAMT (2008); license: CC-by 3.0;
#src
tgt
src:pos
head word
見出し語
nounmultiple-word string
複数語文字列noun
single-word string
単一語文字列noun
canonical form
基準形
noun
convention
慣例
nounfunction word
機能語
noun
superlative
最上級
noun
unmarked sentence order マークなしの文の順序 nounbase form原形
noun
non-base form
非原形
noun
formulate
定型化する
verb
rule of thumb原則
nouncompound複合語
nounmarkerマーカー
noun
upper and lower case
大文字と小文字noun
upper- and lower-case
大文字と小文字のadjective
character文字
nounseparator
分離記号
noun
string
文字列
noun
uninflected
活用されていない
adjective
orthographic
綴りのadjective
lexicographical
辞書編集上の
adjective
Table 4. Sample Entries from System D