1	  CHAPTER 1

INTRODUCTION
1.1 Natural Language Processing
Natural Language Processing (NLP) refers to the computerized approach to analyzing
text that is based on a set of theories and technologies (Liddy, 2001). The author
defined NLP as a “theoretically motivated range of computational techniques foranalyzing and representing naturally occurring texts at one or more levels of linguistic
analysis for the purpose of achieving human-like language processing for a range oftasks or applications”.
The element of levels of linguistic analysis in the definition above is further detailed
into several levels of language: phonology, morphology, lexical, syntactic, semantic,
discourse and pragmatic (Liddy, 2001). While all levels contribute to meaning, mostpeople think that meaning is determined only at the level of semantics.
1.2 Machine translation and Word Sense Disambiguation
Machine translation was the first computerized application related to NLP (Liddy,
2001). Early systems developed for machine translation used simple dictionarylookup for suitable words for translation and merely reordered the words after
translation in order to obey the rules of the target language, disregarding the lexical
ambiguity present in natural language. In the late 1970’s however, attention shifted tosemantics, discourse and communication.
One such concern in semantics is Word Sense Disambiguation (WSD), where muchresearch has been done. Wilks and Stevenson (1996) described WSD as an“intermediate task”, which is necessary at one level or another to accomplish most
NLP tasks.
WSD involves determining the meaning (sense) of a given word in a text thus playing
an important role in NLP. The process usually involves two steps: (1) establishing all
the available senses for the word; and (2) assigning the word to the appropriate sense.
An example can be illustrated with the sentence “Can you give me a hand?” Table 1
below lists all the senses for hand, the definitions and examples.
	
  

	
  

2	
  

Sense

Definition

Example1(Noun) the extremity of the The child washed his hands.
superior limb

2

(Noun) hired laborer on a farm A ranch handor ranch

3(Noun) something written by His hand was illegible.
hand

4(Noun) ability

She wanted to try her hand at knitting.

5(Noun) the cards held in a card Amanda didn’t hold a good hand allgame by a given player at any evening.
given time

6

(Noun) one of two sides of an On the other hand
issue

7

(Noun) a rotating pointer on the The longer hand counts the minutes.
face of a timepiece

8(Noun) a unit of length equal to 4 The horse stood 23 hands.
inches used in measuring horses

9(Noun) a card player in a game We need a 4th hand for bridge.
of bridge

10(Noun) a round of applause to Please give the performers a big hand.
signify approval

11(Noun) the terminal part of the The

kangaroo’s

forearms

seemforelimb in certain vertebrates undeveloped but the powerful five(e.g. kangaroos and apes) “

fingered hands are skilled at feinting
and clouting.

12(Noun) physical assistance

Give me a hand with the housework.

13(Verb) place into the hands or Could you hand me the pepper, please?
custody of

14(Verb) guide or conduct or usher John hand the old man to the bus.
somewhere

Table 1. Senses, definitions and examples of hand

	
  

	
  

3	
  

The above senses for the word hand are taken from the Princeton WordNet (Fellbaum,1998), which will be discussed further in 3.1. As presented, hand has a total of 14
available senses, including two verb senses. The second step of WSD now requires us
to select an appropriate sense that fits the sentence “Can you give me a hand?” In thiscase the most suitable sense is (12). If given the context, speakers of the language will
be able to understand the sentence. However, a computer system would need amechanism of using the context to determine the meaning if it were to depend on
semantic interpretation (McCarthy, 2009).
The assignment of senses to words often relies on two major sources of information:(1) the context of the word being disambiguated, including information contained
within the text or discourse in which the word appears and non-linguistic information
about text such as situation, etc. and (2) external knowledge sources, such as lexical,
encyclopedic or other resources that provide useful data to associate words withsenses (Ide & Véronis, 1998).
WSD is not only a necessity for language understanding applications; it is also helpful
in applications like machine translation where language understanding is not the main
focus (Ide & Véronis, 1998). Machine translation systems benefit from progress in
WSD as it helps to generate more human-like and accurate translation. Other
applications that benefit from WSD include text processing, speech processing,information retrieval and hypertext retrieval, grammatical analysis and content andthematic analysis.
1.3

Translation divergences and translation shifts

Current trends in computational linguistics research tend to deal with the issue of how
to represent meaning. The knowledge that languages present the same information invarious ways is widespread. The differences in language are often addressed intranslation studies, where the study of translation shifts has a long-standing tradition(Cyrus, 2006). In machine translation, these mismatches are studied as translation
divergences (Dorr, 1993) or translation shifts in corpus linguistics.
In translation theories, Vinay and Darbelnet (1958/1995) were prominent with theirapproach to translation shifts. They theorized four types of translation shifts:
	
  

	
  

4	
  

1.

Transposition – a change in word class e.g. scared, an adjective in English,translated to びびる bibiru “to feel frightened”, a verb in Japanese.

2.

Modulation – a change in semantics e.g. thumb to 指 yubi “finger” in Japanese3.

Equivalence – completely different translation, but the meaning is still apparent
e.g. proverbs

4.

Adaptation – change of situation due to disparities in culture e.g. in Japanesesociety, the phrase いただきます itadakimasu is said before a meal. The
expression is used to show appreciation to the plants and animals that gave theirlives for the meal about to be consumed. In addition, it is also used to give
thanks to the people involved in the whole process, which ranges from farmers to
the preparer of the meal. As this practice is not common in the English society,there is no exact translation for the phrase. Instead, it is often loosely adapted as“let’s eat” or “thank you for the meal”.

Catford (1965) described shift as “departures from formal correspondence” between
source and target language. The author distinguished formal correspondence fromtranslational equivalence. The former exists between source and target texts thatoccupy nearly the same place in their respective languages. Translational equivalence
exists when two texts essentially translations of each other. In other words, if thetranslation equivalents are not the exact correspondents, a translation shift is said tohave occurred.
Previous literature on contrastive analysis among languages has also shown thatvarious types of discrepancies can occur between a source and target language (Vinay& Darbelnet, 1958/1995; Marello, 1989; Dorr, 1993). These lexical divergences
between various pairs of languages substantiate that lexicalized conceptual hierarchies
are not universal among all languages. Bentivogli and Pianta (2000) provided a list ofcommon lexical singularities between a source and target language. The
circumstances where translation shifts occur are as follows:1.

Synthetic deviations occur when a translation equivalent does not have the same
synthetic structure as the source language (Dorr, 1993).

2.

Lexical deviations occur when the source and target languages lexicalize asimilar concept with a different word or phrase. Lexical gap, which is an

	
  

	
  

5	
  
absence of a translation equivalent of a word from the source language, isanother form of lexical deviation (Vinay & Darbelnet, 1958/1995; Marello,1989).
3.

Differences in connotation happen when the translation equivalent is unable toreproduce all the implicatures and connotations expressed by a word from thesource language (Brown, Mendes & Natali 1995).

4.

Lastly, denotation differences occur when the denotation of a word from thesource language partially overlaps with the denotation of the translationequivalent (Lo Cascio et al, 1995).

Studies on translation shifts are important for translators to assist them in choosing
suitable translation strategies and to avoid translation mistakes as well as unwarranted
influence of the source to target language (Baker, 1996; Teich, 2003). Whiletranslation shifts may have been a common research area in translation studies, they
have not yet been studied extensively and systematically in corpus linguistics (Cyrus,
2006).
A corpus-based study of translation shifts will be potentially interesting for linguists
as well as translation theorists, who can evaluate the phenomenon empirically. With
this in mind, parallel and comparable corpora play an essential role in such studies, as
they not only serve as the base of intuitions with actual examples but also allow thequantitative studies of translation shifts in different genres and other domain-specificshifts (Čulo et al., 2008).
Studies on translation shifts can also help to improve machine translation systems, as
the occurrence of translation shifts is a major challenge in achieving human-like
translation (Ahrenberg, 2007).
This study aims to not only investigate the translation shifts and describe them but
also to contribute to the area of machine translation research, as data from the study
will be released. This data can then be used in training and improving machinetranslation systems so that their output can resemble human translation.

	
  

	
  

6	
  

This paper is structured as follows. In Chapter 2 we review previous research that has
worked on the relevant topics on annotation and translation mismatches. Chapter 3
introduces a detailed documentation of the resources and methodology taken in thisstudy. The results of the study is presented in Chapter 4 and discussed in Chapter 5.
Lastly, we conclude the study and suggest future works in Chapter 6.

	
  

	
  

7	
  
CHAPTER 2

LITERATURE REVIEW
Several studies have been working with parallel corpora to investigate translation
shifts and cross-lingual divergences. Bentivogli, Forner and Pianta (2004) examinedthe effectiveness of a cross-lingual annotation transfer methodology in theMultiSemCor Corpus, an English-Italian parallel corpus based on the English SemCor
corpus. The authors hypothesized that semantic information is predominantly
preserved during translation process. Based on this hypothesis, annotations can betransferred from the source language to the target language using word alignment as abridge. The researchers found that the main bulk of incorrect transfer of annotation
was due to translation shifts.
Cyrus (2006) worked on FuSe, an English-German parallel corpus with its text
extracted from the EUROPARL corpus (Koehn, 2002) that covers proceedings of theEuropean parliament. The researcher based the approach on predicate-argument
structures as they capture the main piece of information in the sentence and aremostly likely to be represented in both source and target sentences. The predicateswere marked monolingually for part-of-speech (POS) and lemma. The arguments
were given short intuitive role names based on the predicates. After annotation, thepredicates and arguments were then aligned to the corresponding words in the target
language. Translation shifts in the study were categorized on two levels: grammar andsemantics, which were in turn classified into six types respectively. For grammaticalshift, these include
1.

Category change – a change in POS

2.

Passivisation – an active predicate is translated as a passive predicate

3.

Depassivisation – a passive predicate translated as an active predicate

4.

Pronominalisation – common noun or proper noun translated to pronoun

5.

Depronominalisation – pronoun translated to common or proper noun

6.

Number change – change in plurality

The other six types of shifts in the level of semantics are
7.

	
  

Semantic modification – words are not exact correspondence

	
  

8	
  

8.

Explicitation – subcategory of semantic modification. This was assigned when
the target word contained more information than the source word.

9.

Generalization – source word contained more information than target word

10. Addition – a word in the target language that had been added in the translation
11. Deletion – a word in the source language that was not translated in the target
language
12. Mutation – words were translation equivalents but were different in meaning
While the annotation work seemed rather comprehensive, it is also idiosyncratic. Also,
the drawback with this study is that the words were not supported by any semantic
resource such as WordNet. Furthermore, the resource has also not been released anddevelopment has ceased.
LinES is an English-Swedish Parallel Treebank with 2,400 sentences taken from auser manual and a novel (Ahrenberg, 2007). It is different from FuSE as it focuses oncomplete alignments of segment pairs and (semi-)automatic derivation of shifts. Most
importantly, LinES was created with the intention of studying translation shifts interms of syntactic structures. Therefore, annotation was mainly syntactically oriented
rather than semantics.
There is also a German-English parallel and comparable corpus of twelve texts fromeight different genres (Culo et al., 2008). The CroCo corpus contains about 1,000,000
words

and each sentence is annotated with phrase structures and grammatical

functions. Words and phrases are also aligned across parallel sentences. However,like the FuSe, it is also not systematically backed by any semantic resources like
WordNet.
Another parallel treebank, SMULTRON, contains parallel texts mostly in German,
English and Swedish (Volk et al., 2010). It consists of 2,500 sentences from a varietyof genres such as novels, economy texts, a DVD manual and mountaineering reports.
Mountaineering reports were also available in French and a Spanish version of theDVD manual was also accessible. The sentences were tagged for POS and annotatedwith phrase structure trees. The phrase structure trees were also aligned on sentence,phrase and word level. Lemma information was also contained in the German andSwedish monolingual treebanks. An initial study has been done on 50 sentences in the
	
  

	
  

9	
  

English-Swedish parallel subcorpus where the sentences had been annotated withsemantic frames. However, similar to LiNES, the research development is mainly
focused on alignment of syntactic structures.
Padó and Erk (2010) conducted a study of translation shifts on a German-English
parallel corpus of 1,000 sentences from EUROPARL (Koehn, 2002). The sentenceswere aligned at word level and annotated with semantic frames from FrameNet(Baker et al., 1998). The researchers aimed to measure the practicability of frame
annotation projection across languages. The results showed a significant but small
portion of cross-lingual semantic mismatches which equates to translation shifts. Thisis different from the present study, which uses WordNet as the base semantic resource,in which the word senses have a smoother granularity than frames.
Corpus-based studies on translation shifts as illustrated above focused on European
languages. To our knowledge, this present study will be the first corpus-based studyof translation shifts that involves Asian languages – mainly Japanese and Chinese.

	
  

	
  

10	
  
CHAPTER 3

METHODOLOGY
3.1

Resources

3.1.1

WordNets

One of the most challenging problems researchers face in NLP is semantic
information analysis such as WSD as mentioned above in 1.2 (Xu, Gao, Qu & Huang,2008). Thus, a large and computable semantic resource is essential so that machines
can be trained to understand information present in natural language. Machinereadable dictionaries make up a significant portion of these semantic resources and
WordNet is an example of machine-readable dictionaries. In this study, we made useof three WordNets, one for each of the languages.
The Princeton WordNet (PWN) of English is developed and maintained by Fellbaum(1998). It is free and publicly available for download. Since its publication, theWordNet has become the primary source of referent for tasks comprising WSD(Bentivogli, Forner & Pianta, 2004). Words are arranged in terms of synonyms andare referred to as synsets. The synsets are further linked to one another throughvarious lexical and semantic associations. Relationships among words in the WordNet
are mainly built on synonymy but due to its structure, it also allows users to see theconceptual relationship between words such as hyponymy, holonymy, taxonomy andso on (Fellbaum, 2005). Figure 1 below illustrates some of the concepts associatedwith the word table.

furniture
03405725-n

Holonym oftabletop
04381860-n
Ha

ns
si

ta

e
nc

table

lamp

04379243-n

03636649-nround_table
04114554-n

kitchen_table

operating_table

03620967-n

03850492-n

Figure 1. WordNet representation of table

	
  

	
  

11	
  

The word table tagged with 04379243-n is a direct hyponym of furniture and direct
hypernym of kitchen table, operating table and others not shown here. It is a holonymof tabletop and has an instance round table. A holonym is the name of the whole ofwhich contains a part Y. Meronym is the term for the constituent that makes up thewhole. Thus, if X is part of Y, Y is a holonym of X and X is a meronym of Y. An
instance is a proper noun that refers to a singular referent and is a specific form ofhyponym.
The Japanese WordNet (JWN) was first published in 2009 (Isahara, Bond, Uchimoto,Utiyama and Kanzaki, 2008). Synsets in the JWN are based on the same lexicalarrangement as the PWN. This implies that lexical units in the JWN are also arranged
according to their hierarchical relations among words. Ongoing work includes adding
synsets that are not in the PWN and also modifying the structures in the hierarchy sothat they make a better representation of the Japanese language.
Like the JWN, the semantic hierarchy in the Chinese WordNet (CWN) also derives
from PWN (Xu, Gao, Pan, Qu and Huang, 2008). The researchers translated theoriginal English WordNet into Chinese using automatic translation approaches.
Several other linguistic resources were used to build the WordNet, such as American
Heritage Dictionary and X-Dict Dictionary.
While the JWN and CWN are built with their structures in accordance to the PWN,
there are still gaps between the three WordNets. One reason is that the JWN and
CWN are less developed compared to the PWN. Table 2 shows a summary of each
WordNet.
Princeton WordNet

Japanese WordNet

Chinese WordNet

Synsets

117,659

57,238111,045

Words

155,287

93,834

115,136

Senses

206,941

158,058

168,824

Table 2. Summary of number of synsets, words and senses in each WordNet

	
  

	
  

12	
  

As Japanese and Chinese lemmas are simply added to existing synsets in the PWN,
there are some synsets in the JWN and CWN that are not yet represented in the PWN
and vice versa. This is often due to the uniqueness of the languages (Bond et al.,2009).
In addition to the lack of representation of certain concepts in the WordNets, wewould also like to call to attention the minor errors that are present in the WordNets.
For example, in the CWN, the POS tag for some words may be incorrect, such as 睡眠 shuìmián “sleep” is tagged as a verb instead of noun. There are also instanceswhere a wrong lemma is included in the synset. In view of this, we hope to report andcorrect these errors as we spot them so as to help improve the WordNets.
3.1.2

Corpus

We conducted this study with parallel and aligned versions of “The Adventure of theDancing Men”, part of the Sherlock Holmes short stories written by Sir Arthur Conan
Doyle. As the copyright for the original text has expired, redistributable translationsin Japanese and Chinese were readily available for use.
This parallel tritext is part of the NTU Multilingual Corpus that is currently being
developed (Tan & Bond, 2011). Using original English text as the source language,
the Japanese and Chinese texts were first aligned at the sentence level. The texts were
tokenized and automatically tagged for POS and lemma. They were then manuallysense-tagged with reference to the respective WordNets. Table 3 below shows thecomposition of each text.
EnglishJapanese

ChineseSentences

599

698

680

Words

6,842

5,246

5,148

Concepts

11,19813,483

11,325

Table 3. Summary of number of sentences, words and concepts in each text

	
  

	
  
3.2

13	
  
Annotation of relationship between words

In this study, we worked on the available tagged data. The initial word alignment wasdone using a program that checks each word in a sentence pair. The program links theword if(i)they are the same synset e.g. say and 言う iu “to say”, both tagged with thesynset ID 00979870-v, defined as “utter aloud”(ii)

they are direct hypernyms or hyponyms e.g. wash, 01535246-v, defined as“cleanse with a cleaning agent, such as soap, and water” is a hypernym of 洗

い落とす araiotosu “to wash out”, tagged to 01535742-v and defined as“wash free from unwanted substances, such as dirt”.
Mok (2012) tagged the relationship between words in the English and Chinese data.
Different symbols were used to show the different relationships between source andtarget words. We used the tagging guidelines in Mok (2012) as a base to establish therelationship between the words in the English and Japanese data.
For words which share the same synset, “=” is used to show the link between them.
For example, notebook is tagged with the synset 06415419-n, defined as “a book withblank pages for recording notes or memoranda”. In the Japanese version, this concept
is translated to

備忘録 bibouroku “notebook” with the same synset 06415419-n.

Hence, we linked the words with the “=” symbol.
When the target word is the direct hyponym of the source lemma (English), “>” isused to show the link. For example, finger, tagged with 05566504-n, defined as “anyof the terminal members of the hand (sometimes excepting the thumb)”, is a direct
hypernym of 人 差 し 指 hitosashiyubi “index finger”, tagged with 05567381-n,
defined as “the finger next to the thumb”. This relationship is tagged with “>”.
Likewise, if it is the direct hypernym, “<” is used to show the link. An example issunlight, tagged with 11485367-n, defined as “the rays of the sun”, is a direct
hyponym of 光 hikari “visible radiation”, tagged with 11473954-n, defined as“electromagnetic radiation that can produce a visual sensation”. This relationship ismarked with “<”.
	
  

	
  

14	
  

For words with related meaning that are not identical, the symbol “~” is used to show
the link. This includes instances like meronymys, antonyms and similar relationships
other than direct hyponymy. One example would be notebook, tagged with 06415419n, and メモ帳 memochou “notepad” tagged with the sense 15021085-n, defined as “apad of paper for keeping notes”. In WordNet, these two synsets are not related in any
hyponymy nor meronymy relationship. However, they do share a similar meaning as
both refer to an object for recording notes. Therefore, we linked the words together
with “~”.
An additional instance would be words with POS gaps such as dull, an adjective
tagged with 00393992-a, defined as “(of color) very low in saturation”. In theJapanese text, this is translated to くすんだ kusunda “darken”, a verb tagged with
00312380-v, defined as “become dark or darker”. In this case, they were also linked
with “~”.
In addition to the four symbols, we introduced a new symbol “:” to indicate
combinations of words or phrases that are translation equivalent of the original source
but are not lexicalized enough to be linked. One example is shown in the phrase
below(1) English: becontent

with

my

word

Japanese: わたくし

の

言葉 を

信じ -て

watakushinokotoba wo

shinji-te

1SG

POSS

word

to believe-te

ACC“believe in my words”
In the example, we can perceive the link between individual concepts. However,while we comprehend that the Japanese version is a translation equivalent for theoriginal English text, we would not want to say that content is of similar or related
meaning to the lemma 信じる shinjiru “to believe”. Generally speaking, these twowords are not clearly linked lexically. However, to show that in this context they are
somewhat related, we used the symbol “:” to show the connection.

	
  

	
  

15	
  

Another instance would be in the following phrase(2) English: several fresh dancing men pictures
Japanese: 何

枚

か

新しい

踊る

人形

nan

mai

ka

atarashii

odoru

ningyou

DET

CL

Q

new

to dance

figure“several pieces of new dancing men (pictures)”
In classifier languages, a noun can sometimes be deleted and referred to by theclassifier when the reference to it is repeated (Denny, 1986). In example (2) above,the classifier 枚 mai, used for thin, flat objects is used to represent the concept of
pictures. While the two words are related in a classifier-noun connection, we do notwant to say that they are exact correspondents of each other. Thus, we used “:” todenote it.
For direct antonyms, we used the symbol “!” to represent the relationship. A simple
example would be hot translated to 寒くない samukunai “not cold”, whereby thelemma is 寒い samui “cold”.
We used another symbol, “#” to represent weak antonyms, which were observed more
often than direct antonyms in the English-Japanese pair. One such example is propose,tagged to 00708980-v and defined as “propose or intend” in the sentence below(3) English: “you do not propose to invest in South African securities?”
Japanese: 「君 は、

南-アフリカ

の

証券

へ

の

kimi

wa

minami-afurika

noshouken

he

no

2SG

NOM

south-africa

POSS

securities

DAT

NMZ

投資

を

思いとどまった。」

toushiwo

omoitodomat-ta

investment

ACC

to hold back-PST“You held back investment in South African securities?”

	
  

	
  

16	
  

In the Japanese text, propose was translated to the lemma 思 い と ど ま る
omoitodomaru, tagged to the synset 00613393-v and defined as “abandon idea orclaims; stop maintaining or insisting on”. This synset represents a meaning oppositethat of propose. However, as this is unlike a definite antonym such as hot and cold,
we interpreted this as a weak antonym and assigned the symbol “#” to show the
relationship.
We gave a confidence level of 95% to each hand-tagged relationship. Table 4 shows abrief summary of the symbols used to annotate the relationships. In this study, atranslation shift has occurred when concepts are linked with symbols other than “=”.
SymbolRelationship=Same synset>

Direct hyponym of source language<

Direct hypernym of source language~

Similar or related meaning, including different POS:

Translation equivalent but not clearly linked lexically!

Direct antonym#

Weak antonym

Table 4. Symbols used to indicate relationships between words
In addition to tagging relationships between content words, we also showed
relationships between interjections, pronouns-proper or common nouns and vice versa,
as illustrated in the phrase below.
(4) English: “So, Watson,” said he, suddenly,
Japanese: 「だからワトソンー」と

ホームズ

が

突然

Dakarawatoson-toho-muzuga

totsuzensowatsontoholmesNOM

suddenly

口

を

ひらく。

kuchi wohirakuopen

to open

ACC“’So, Watson,’ Holmes opens his mouth suddenly.”

	
  

	
  

17	
  

In the example above, we linked so with だから dakara “so” and he with ホームズ
ho-muzu “holmes”. Both concepts are linked with “~”, with the comments“interjection” and “pronoun-proper noun” respectively. For pronouns and some of theinterjections, the WordNets do not have a synset for the concepts. The reason why weare linking these concepts is to show that they are translation equivalents. Also, it
helps users of the data to see that pronominalisation occurs during translation and viceversa depending on the translating style.
3.3

Tagging issues

This section documents some of the tagging issues we came across as we were
tagging the relationships between words and concepts for the English-Japanese pair.
Tagging issues with the English-Mandarin pair have been discussed in Mok (2012)
and will not be presented in detail here.
3.3.1

Changes to original tags

As we were going through each sentence pair, we found that there were errors in theprevious semantic tagging for some of the words. We corrected these as we
progressed with the tagging. For example, club in the phrase, “returned from the clublast night”, was initially given the synset 03054311-n, defined as “a building that is
occupied by a social club”. The Japanese translation クラブ kurabu “club” was given
the synset 02931417-n. This synset is defined as “a spot that is open late at night andthat provides entertainment (as singers or dancers) as well as dancing and food anddrink”. Both the semantic tags given to the words are legit, however, there is anothersense that fits the context more accurately. This is the synset 08227214-n, with thedefinition “a formal association of people with similar interests”. Therefore, we
corrected the original tags for both and linked the words with “=”.
We also tried to tag words with “=” whenever possible. Therefore, when the tagged
sense for one word is appropriate for the corresponding word, we changed it
accordingly. For instance, key was originally tagged with the synset 06424869-n,
defined as “a generic term for any device whose possession entitles the holder to ameans of access”. The Japanese equivalent 鍵 kagi “key” was tagged with synset
03613294-n, defined as “metal device shaped in such a way that when it is inserted

	
  

	
  

18	
  

into the appropriate lock the lock's mechanism can be rotated”. Both senses areappropriate for either word. However, the synset 06424869-n is not available in the
JWN. On the other hand, the synset 03613294-n is available in both PWN and JWN.
Thus, we changed the original tagged sense for the English word from 06424869-n to03613294-n, and linked the words with “=”.
We also changed the original tags for some words that were tagged with “s” which
denotes a missing sense. This is quite often the case with Japanese words that wererepresented in hiragana only. For example, in the phrase below(5) English: toJapanese: キュー

steady thecue

がすべら-ない よう

kyu-

ga

subera-nai

you

cue

NOM

to slide -NEG

in order“so that the cue does not slide”
The lemma for すべら subera, すべる suberu, was initially tagged with “s”. Thiswas because the lemma in its hiragana form is not listed in the JWN. However, 滑る
suberu “to slide” which is a kanji representative for the word, is listed and a suitable
synset 01870275-v is also available. This synset is defined as “move obliquely or
sideways, usually in an uncontrolled manner”. As the synset is appropriate for thelemma, we corrected the original tag from “s” to 01870275-v. Also, as we deemed す

べら subera “to slide”, together with the negative marker ない nai, as a translationequivalent for steady but not clearly linked lexically, we linked the two words with
“:”.

	
  

	
  

19	
  

In Japanese, it is not uncommon to find a concept represented with a multiword
expression, as exemplified by example (6).
(6) English: saidhesuddenly

Japanese: ホームズ

が

突然

口

を

開く

ho-muzuga

totsuzenkuchi wohirakuHolmesNOM

suddenly

mouth ACC

to open“Holmes opens his mouth suddenly”
This concept could be replaced with a simple verb 言う iu “to say”, just like theoriginal English text. However, the translator chose to use a multiword expression.
The tokenizer did not recognize this as a multiword concept previously. As thisconcept is lexicalized, we grouped the words together and tagged the new concept tothe synset 00941990-v, where the lemma “speak” is defined as “express in speech”.
The concepts were then linked together with “~”.
3.3.2 Changes to the tagged Chinese data
Although the English-Chinese data was tagged before the English-Japanese data,tagging was not complete and many words that could be linked were not tagged. In
addition, we introduced the two symbols “:” and “#” after the English-Chinese data
was tagged. Hence, we retagged the English-Chinese corpus and checked through therelationship between words when the English-Japanese data was completed. Unlikewith the English-Japanese data, changes were only made to the Chinese tagged sense
if they can be linked with “=” to the English correspondent.
We also created new concepts for words that are lexicalized in the Chinese language
but not represented in the CWN. This includes words like 桌 zhuō “table” which isoften used in combinations like 桌椅 zhuōyǐ “tables and chairs” and 桌上 zhuōshàng“on the table”. In the CWN, only 桌子 zhuōzi “table” is listed as a lemma for table,
tagged to 04379243-n, defined as “a piece of furniture having a smooth flat top that is
usually supported by one or more vertical legs”. As 桌 zhuō “table” is well-

	
  

	
  

20	
  

established, we deemed it appropriate to include the lemma into the synset. Hence, aconcept is created for the word and tagged to the synset 04379243-n.
3.3.3

Concepts that could not be linked directly

There are many cases where words cannot be linked directly. Other than grammatical
divergences, there were instances where the words belong to the same POS but due tothe subtle, additional meaning in the words, the concept cannot be said to be exacttranslation of each other. This is illustrated in example (7).
(7) English: “How on earth do you know that? I asked.
Japanese: 「いったい、どうしてその こと を？」と、 私

ittai,

doushite

sono

koto

wo

to

watashion earth

why

that

thing

ACC

QUOT

1SG

は

聞き返す。

wa

kiki-kaesu

NOM

to ask in return“’Why on earth, (do you know) that thing?’ I ask in return.”
In the above, the corresponding word in the Japanese sentence has an additional
meaning of “to ask in return” while in the original English sentence it is just “to ask”.
Although these two words are very similar in meaning, we were apprehensive to saythat they are exact translations and therefore did not link them directly with “=”.
Another example involves light verbs and their nouns, such as in the example (8)below where the Japanese multiword expression 身を震わせる mi wo furuwaseru “totremble convulsively, as from fear or excitement” is a translation equivalent of gave astart. However, as gave is a light verb with no significant semantic content of its own,
we linked the Japanese verb to the noun start with the symbol “~” instead of linking
the words directly.

	
  

	
  

21	
  

(8) English: I gave a start of astonishment.
Japanese: 私

は

驚き

の

あまり

watashiwa

odoroki

no

amari

1SG

NOM

astonishment

POSS

much

身 -を -震わせ -た
mi-wo-furuwase-ta
body-ACC-to shake-PST“I shook my body (due to) much astonishment.”
3.3.4

Concepts that could not be linked

One of the most common cases where concepts could not be linked is when the words
to be annotated are not present in the aligned corresponding sentence. There areseveral cases in the data where a sentence in English is translated into two or moresentences in the other language, and vice versa. This is exemplified by the sentence
below, where the first clause of the sentence right until the word suddenly
corresponds to (a) in the Japanese text and the remaining clause corresponds to (b).
(9) English: “So, Watson,” said he, suddenly, “you do not propose to invest in South
African securities?”
Japanese: (a)「だから ワトソンー」と

ホームズ

が

突然

dakarawatoson-toho-muzuga

totsuzensowatsonQUOTholmesNOM

suddenly

口

を

ひらく。

kuchi

wo

hirakumouth

ACC

to open“So, Watson,” said Holmes suddenly.
(b)「君 は、

南-アフリカ

の

証券

へ

の

kimi waminami-afurika

noshoukenheno

2SG NOM

south-africa

POSS

securities

DAT

NMZ

投資

を

思いとどまった。」

toushiwo

omoitodomat-ta

investment ACC

to hold back-PST“You held back investment in South African securities?”
	
  

	
  

22	
  

Another instance where linking is impossible is where the translated sentence orphrase does not match the original text at all, as illustrated in the following example.
(10) English: Holmes had been seated for some hours in silence with his long, thinback curved over a chemical vessel in which he was brewing aparticularly malodorous product.
Japanese: ホームズ

は黙り込ん-だ まま、その 細く 長い

ho-muzuwa

damarikon-da mama sono

hosoku nagai

holmesNOMin silence-PST while

DET

thin

身体

を

猫背

に

して、

何

shintaiwo

nekozeni

shi-te

nan

body

ACC

cat’s backDAT

to make-CONJ DET

時間 も

化学

実験室

に

向かってい-た。

jikan

mo

kagaku jikkenshitsu ni

mukattei-ta

hour

INT

chemical laboratory

to face-PST

LOC

long

“While Holmes was in silence, he made his thin long body into a cat’sback, and faced the chemical laboratory for several hours.”
In (10), the final clause (underlined) in the Japanese sentence was very looselytranslated and roughly means “faced the chemical laboratory”. However, in theoriginal text, this meaning was not inherent at all. As a result, the Japanese words inthe final clause are left without any suitable corresponding words in the original text.
Similarly, the underlined phrase in the English sentence above does not correspond to
any words in the translated Japanese text.
The following two examples illustrate another occurrence where linking is difficult.
This usually occurs where the sentence as a whole is a translation equivalent but due
to the choice of words and translation style, it is impossible to link the words at all.

	
  

	
  

23	
  

(11) English: “I can not say that she did not give me every chance of getting out of it
if I wished to do so.”Japanese: 私が訊ね さえ すれ-ば、

包み隠さ-ずwatashigatazune sae

sure-batsutsumikakusa-zu

1SG

NOM

to ask if

to do-COND

to conceal-NEG

言って

くれ-た

と

思って-い-ます。

ittekure-tatoomotte-i-masu

to sayto give-PST

QUOT

to think-PTCP-POL“I think (that) if I ask, (she) will not conceal (anything) and tell me.”(12) English: I am.
Japanese: まったくだ。

mattakudaabsolutely

COP“Absolutely”
In (11), one can infer the translation equivalence just by looking at the sentences
themselves. In example (12), one would have to look at the context in which thesentence appeared in order to understand how they are related. In both examples,
although the meaning of the original English sentences is reflected in the Japanesetranslated sentences, there is no possible way of linking any words together.
In the following example, linking relationship between translation equivalents wasdifficult, as the concepts are not listed in the WordNets.
(13) English: “I am sure that I shall say nothing of the kind.”
Japanese: 「いやいや、そんなiyaiyasonna

by no means that kind of

こと は

言わ-ん

よ」

koto

wa

iwa-n

yo

thing

SBJ

to say-NEG

yo“no no, I will not say that kind of things”
In the Japanese text, the word いやいや iyaiya “by no means” in this context wasinterpreted as more colloquial in which a double negation was used, such as in “no no,

	
  

	
  

24	
  

that is not it.” As this sense was not included in the WordNets, we were unable to
make any relationship links to the words.

	
  

	
  

25	
  
CHAPTER 4

RESULTS
Having linked and tagged the relationships between words, we proceeded to count the
number of each type of link. Table 5 and 6 below give a summary of the distinct
concepts, synsets and linked synsets for each of the parallel corpus.
EnglishJapanese

Distinct Concepts

6,587

5,119

Distinct Synsets

5,125

4,433

Distinct Linked Synsets

2,542

2,535

Table 5. Summary of English-Japanese corpus
In the English-Japanese corpus, 49.60% and 57.18% of the distinct synsets werelinked in the English and Japanese texts respectively.
EnglishChineseDistinct Concepts

6,587

5,143

Distinct Synsets

5,125

4,194

Distinct Linked Synsets

2,607

2,608

Table 6. Summary of English-Chinese corpus
In the English-Chinese corpus, 50.87% of the English distinct synsets were linked and62.18% of the Chinese distinct synsets were linked. This is 1.27% and 5% more thanthe English-Japanese data. This could be due to the fact that the CWN contains more
synsets than the JWN, so more synsets can be linked with those in the PWN.
Table 7 below shows the number of each type of link in the corpora. In both corpora,more than half of the linked concepts are tagged with “=”, that is to say they are exact
correspondents of each other. Relationships marked with “~” also take up asignificant percentage in both corpora, 36.07% in the English-Japanese and 30.25% in
the English-Chinese. Compared to the English-Chinese corpus, there are moreinstances where the target Japanese word is a direct hypernym of the English source

	
  

	
  

26	
  

word. In both corpora, direct antonyms were used very sparingly. In total, there are2745 linked concepts in the English-Japanese corpus and 2850 linked concepts in the
English-Chinese corpus.
Link types / Relationship

English-Japanese%English-Chinese=

Same synset>

Direct hyponym

75

2.73

94

3.30

<

Direct hypernym

63

2.30

39

1.37

~

Similar/related

990 36.07

862 30.25:

Translation

186

6.78

128

4.49

equivalent

1,416 51.58%

1,712 60.07!

Direct antonym

1

0.04

2

0.07

#

Weak antonym

14

0.51

13

0.46

2,745

100

2,850

100

Total

Table 7. Number of each link types in the corpora
Among the concepts that are linked with “~”, a number of them can be identified into
certain categories such as pronominalisation, derivations etc. The table below shows
the number of these identified types of relationships tagged with “~”.
English-Japanese%English-Chinese%

Pronominalisation

0

0.00

7

0.81

Depronominalisation

86

8.69

22

2.55

Holonymy relationship

12

1.12

0

0.00

Derivation

56

5.66

30

3.48

2nd level hyponym of source

8

0.81

13

1.51

2nd level hypernym of source

10

1.01

18

2.09

Table 8. Identified types of relationships tagged with “~”We found no instances of pronominalisation in the English-Japanese corpus. In the
English-Chinese corpus, 0.81% of the concepts linked with “~” were found to be of
pronominalisation. On the contrary, depronominalisation occurred more often in the
English-Japanese corpus than the English-Chinese corpus. Derivations that could be

	
  

	
  

27	
  

identified make up 5.66% and 3.48% of the concepts linked with “~” in the EnglishJapanese and English-Chinese corpus correspondingly. For example, the verbs 答え

る kotaeru “to answer” in Japanese and 回答 huídá “to answer” in Chinese are thederived forms of the noun answer in English.
Table 9 gives a summary of the linked concepts and their POS tags. We included all
linked concepts except those linked with “=”, since they are exact correspondents ofthe target words. In addition, we only count for those concepts where both source andtarget words had WordNet synsets.
POS Gap

English-Japanese%English-Chinese%

Noun-Noun

386

32.80

347

31.98

Noun-Adjective

25

2.12

42

3.87

Noun-Verb

52

4.42

79

7.28

Noun-Adverb

11

0.93

24

2.21

Adjective-Adjective

72

6.12

103

9.49

Adjective-Noun

93

7.90

40

3.69

Adjective-Verb

33

2.80

27

2.49

Adjective-Adverb

28

2.38

33

3.04

Verb-Verb

298

25.32

274

25.25

Verb-Noun

87

7.39

16

1.47

Verb-Adjective

2

0.17

7

0.65

Verb-Adverb

7

0.59

10

0.92

Adverb-Adverb

40

3.40

60

5.53

Adverb-Noun

20

1.70

3

0.28

Adverb-Adjective

18

1.53

18

1.66

Adverb-Verb

5

0.42

2

0.18

1177

100

1085

100

Total

Table 9. Summary of linked concepts and their POS tags, excluding concepts linked
with “=”From the table, 67.64% of the linked concepts in the English-Japanese corpus wheretranslation shifts had occurred share the same POS tags in both source and target
	
  

	
  

28	
  

language. In the English-Chinese corpus, 72.25% of the linked concepts share thesame POS tags.
In the English-Japanese corpus, 7.90% syntactic mismatches occurred between
English adjectives and Japanese nouns and 7.39% occurred between English verbs
and Japanese nouns.
On the other hand, syntactic mismatches in the English-Chinese corpus are mainly
observed between English nouns and Chinese verbs (7.28%). This is followed bymismatches between English nouns and Chinese adjectives at 3.87%.
In both corpora, mismatches between verb-adjective, verb-adverb, and adverb-verb do
not occur frequently.

	
  

	
  

29	
  
CHAPTER 5

GENERAL DISCUSSION
In this section, we will analyze and discuss some of the translation shifts that were
found in the data.
In some of the sentences, we found holonym relationships in some of the sentences,such as eye and 瞳 hitomi “pupil” in Japanese. Eye bears a two level holonym
relationship with pupil in the WordNet system (Figure 2). In another example, back isa meronym of torso which is a meronym of 身体 shintai “body” in Japanese. As there
are exact correspondents of these in the Japanese language, 目 me “eye” and 背中
senaka “back”, we attribute the semantic shifts to translating style.

目 me “eye”
Holonym of05311054-n

虹彩 kousai “iris”
05319936-n
Holonym of

瞳 hitomi “pupil”
05320183-n

Figure 2. Holonymy relationship between eye and pupil
Lexical divergence was also found in translation equivalents of nouns derived fromnames of countries, as in American from America. In English, the word American canbe interpreted as a person from America, or of things relating to the language orculture of America. On the other hand, in Japanese, if one were to refer to people,
language or culture relating to a country, a suffix is often necessary. Thus, an
American would be referred to as アメリカ人 amerikajin in Japanese, where thesuffix 人 jin indicates a person. Likewise, the concept American car would betranslated to Japanese as アメリカの車 amerika no kuruma where の no is a particle

	
  

	
  

30	
  

indicating POSSESIVE. These words were tokenized separately, for example and 人 jin“person” in Japanese. Therefore, when linking the words, we could not treat them as
exact correspondences but linked with “~” instead, and these contributed to themismatches. In addition, アメリカ amerika “America” is a holonym of the word
American which bears the sense “native or inhabitant of the United States”.
In Chinese, the words that represent the names of countries can directly act as a noun
modifier. For example, the word 美国 měiɡuó “America” in 美国人 měiɡuó rén “anAmerican” and 美国车 měiɡuó chē “American car” has the sense “of things relating
to the person, language or culture of America”. It is also common in Chinese to use
the particle 的 de to describe the preceding noun, such as in 美国的车 měiɡuó de chē“American car”.
Translation shifts may occur when a word has gone through a semantic change in thesource language but not the target language. We found an example in the EnglishJapanese corpus. The word kill underwent meiosis, a change from stronger to weaker
meaning. In the PWN, there is a sense for kill with a synset ID 02198819-v, which isdefined as “be the source of great pain for”. An example of such a use is “These newheels are killing me.” However, the translation equivalent for kill in the Japanesetranslated text, 殺す korosu “to kill”, does not have that synset nor a similar sense.
The synset tagged to the Japanese word is 01323958-v, defined as “cause to die”. We
could only link the words as of having related meaning and therefore, a shift has
occurred.
In the corpora, the original English text used eye throughout regardless of whether it
was with reference to the organ of sight or the expression in one’s eye. However, theChinese translator made a distinction between them and translation shifts occurred asa result. The examples below illustrate three distinctions that were made in the
Chinese text.

	
  

	
  

31	
  

(14) English: I could see by his eyes
Chinese: 从

他

眼神

中

可以 看出

cóng

tā

yǎnshén

zhōng kěyǐkànchū

from3SG

eye-expression

in

see

can

“can see from the expression in his eyes”(15) English: earnest blue eyes
Chinese: 诚实

的

蓝

眼睛

chéngshídelányǎnjīng

honest

de

blue

eyes

“honest blue eyes”(16) English: his eye rested upon the paper
Chinese: 目光

落在

那

张

纸条

mùguāngluòzàinàzhāng zhǐtiáo

shàng

gaze

to fall on

that

CL

on

paper

上

“(his) gaze fall on that piece of paper”
The translation shifts observed here are attributed to the translator’s effort to be more
specific.
As illustrated in 3.3.2 example (7), divergences also occur when a word in one
language contains more information than a word in the other language. Combining
two verbs forms many words in Japanese, for example 食べる taberu “to eat” and みる miru “to see” are combined to form 食べてみる tabetemiru “to try eating and see”.
In the corpora, compound verbs like these are used rather frequently in the Japanesetext, such as in example (7), which reflects the agglutinative nature of Japanese
language.
In Chinese, two verbs often concatenate and form a single, lexicalized verb. In thefollowing examples, the verbs are composed of two single verbs.

	
  

	
  

32	
  

(17) English: he will not try to escape
Chinese: 他

不

会

逃跑

的

tā

bù

huì

táopǎo

de

3SG

NEG

will

to escape-to runde“he will not escape (and run)”(18) English: I held my gun to scare him off
Chinese: 我

举起

枪

wǒ

jǔqǐ

1SG

to hold up

想把

他

吓跑

qiāng xiǎngbǎ

tāxiàpǎogun

3SG

to scare-to run

to want to“I help up (the) gun (to want to) scare him (and make him run)”In (17) the verb 逃跑 táopǎo “to escape and run” consists of two verbs. The first verb

逃 táo already contains the meaning of “to escape” on its own, and the second verb 跑pǎo is there to add the further information of “to run”, perhaps to show the urgency ofthe escape. Both verbs take the subject as arguments. In this context, escape wastagged to the synset 02075049-v, defined as “flee”. The Chinese counterpart also
shares the same synset and thus the words were taken to be exact translations of eachother.
On the contrary, in (18), the first verb 吓 xià “to scare” takes both the subject andobject as arguments while the second verb 跑 pǎo “to run” only takes only the object
as an argument. The second verb shows an action that was caused by the action
indicated by the first verb. The word scare and the multiword expression scare off
were both tagged to the synset 017857480-v, which is defined as “cause to losecourage”. However, the target word 吓跑 xiàpǎo, which a native speaker might eventranslate as “to scare off”, was not represented in the CWN. Nonetheless, we linked
scare and 吓跑 xiàpǎo “to scare someone off” with “~” as the Chinese word evidently
contains more information than the English word. We also made a comment that asynset for 吓跑 xiàpǎo “to scare someone off” should be created in the CWN.

	
  

	
  

33	
  

In addition, a verb and an adjective can also combine to form a new Chinese verb aslong as the resulting state described by the adjective is possible (Palmer & Wu, 1995).
The function of verb-adjective words is usually to express a change-of-state event
such as breaking. The adjectival component of the verb usually conveys the resulting
state more explicitly than it is normally done with English. The productivity of such
verbs in both Japanese and Chinese may contribute to translation divergences.
Adjectives that are made up of two separate adjectives are also common in the
Chinese language. For example, in the following phrase, the English words long andthin were translated into a single Chinese word 瘦长 shòucháng “lanky”.
(19) English: his long, thin back curved overJapanese: 他

弯

着

瘦长

的

身子

tā

wān

zhe

shòuchángdeshēnzi3SG

to curve

PROG

lanky

de

body

“he curved (his) lanky body”
In (19), the word 瘦长 shòucháng “lanky” corresponds to long and thin in the original
English text. In annotating the relationship, we had to link the words separately. Since

瘦长 shòucháng “lanky” contains more information than long and thin individually,
we considered it a shift that had occurred during translation. In fact, 瘦长 shòucháng“lanky” is made up of two words 瘦 shòu and 长 cháng which means “thin” and“long” respectively. The translator could have chosen to make an exact translation
using a conjunction to join the two concepts. However, as the word 瘦长 shòucháng“lanky” is lexicalized, this may have influenced the decision to use the lexicalized
term instead.
The verb する suru “to do” in Japanese can be used to convey different meanings
depending on the words they are combined with. One of its main functions is tochange a word into a verb. Verbs are closed classes in Japanese and they do notreadily add new members (Bloch, 1946). New and borrowed verbs are conjugatedperiphrastically as nouns + する suru “to do”, such as 結婚する kekkonsuru “to

	
  

	
  

34	
  

marry” and 約束する yakusokusuru “to promise”. Since Japanese has a large
vocabulary of Chinese loanwords (Shibatani, 1990), it can be expected that many
verbs in Japanese are formed using noun + する suru “to do”. This nature of theJapanese verb system contributes to the number of syntactic shifts we found in thedata where verbs in English were linked to nouns in Japanese.
There are also cases where an established multiword expression in English could not
be translated directly into the target language. Examples (20) and (21) below illustrate
the two instances where the multiword expression, to the bottom was used in the text.
(20) English: get to the bottom of it
Japanese: 暴く

こと が

できます

abaku

koto

ga

deki-masu

to expose

NMLZ

NOM

to be able to-POL“able to expose”
Chinese: 彻底

弄

清楚

chèdǐ

nòng

qīngchǔ

completely

to makeclear“to make clear completely”(21) English: sift the matter to the bottom
Japanese: 最後 まで

調べ-たいsaigo made shirabe-tai
end

until

to investigate-to want

“want to investigate until the end”
Chinese: 彻底

弄

清楚

chèdǐ

nòng

qīngchǔ

completely

to makeclear“to make clear completely”
From the examples, the Chinese translator was consistent with the translation of to thebottom in both instances, suggesting that the expression 彻底弄清楚 chèdǐ nòngqīngchǔ “to make clear completely” behaves in the same way as the English
	
  

	
  

35	
  

multiword expression and is an established multiword expression. The word 彻底chèdǐ “completely” in particular seems to incorporate the meaning of to the bottom on
its own. In the Japanese text however, the translator used two different approaches totranslate the concept of to the bottom. The lexical divergence that presents itself here
could be due to the absence of a Japanese equivalent to the multiword expression in
English.
The translator of the Chinese text also used idiomatic expressions to express certain
concepts. These idiomatic expressions transmit the same idea as the original text but
they occasionally function as different POS, or even contain much more informationthat is not reflected in the English counterpart.
One example is 作恶多端 zuòèduōduān “to have done many kinds of evil”. This
idiomatic expression was translated from the English word evil. In the context, evil
was used to refer to the acts that had been done. The Chinese idiomatic expression
however, was used to describe the agents who did the act itself. Hence, a syntactic
shift occurred here from a noun in the English original text to an adjective in the
Chinese translated text.
We found a lot more depronominalisation going on between the English-Japanese
corpora than the English-Chinese corpora. Although both Japanese and Chinese are
pro-drop languages, pronouns are used much less frequently in Japanese than in otherlanguages (Maynard, 1990). In addition, Japanese speakers prefer to refer to anotherperson by title, function or by that person’s name. In Chinese however, it is common
to use pronouns to refer to another person. The occurrence of depronominalisation
and also pronominalisation contributed towards a higher count for relationships
tagged with “~”.

	
  

	
  

36	
  

(22) English: Oh, that’s your idea!
Japanese: ほう、そう 考える

か

ね

hou

sou

kangaeru

ka

ne

oh

that

to think

QUOT

eh“Oh, (you) think that eh?”
Chinese: 噢，

那

是

你

的

想法

o

nà

shì

nǐ

de

xiǎngfǎ

oh

that

is

2SG

POSS

idea

“Oh, that is your idea”(23) English: She shot him and then herself
Japanese: 奥 -さん

が旦那 -さんを撃って、

oku-sangadanna-san

wo

utte

wife-HON

NOM

husband-HON

ACC

to shoot at-CONJ

それから

自分 も

撃った

sorekara

jibun moutta

and thenself

to shoot at-PST

too“(the) wife shot (the) husband and then shot (her)self too”
Chinese: 她

拿

枪

tā

ná

qiāng xiān

3SG

to take gun

然后

打

自己

ránhòudǎzìjǐand thento shoot

self

先
first

打

丈夫，

dǎ

zhàngfū,to shoothusband“She took the gun to first shoot (her) husband, and then shot (her)self”
In (22), the pronoun your was dropped in the Japanese translation but kept in the
Chinese translation with a corresponding pronoun and POSSESIVE marker, 你的 nǐde“yours”. In (23), depronominalisation occurred in the Japanese text as she and him
were translated to the common nouns 奥さん okusan “wife” and 旦那さん dannasan“husband” respectively. The suffix さん san acts as a honorific marker. In the

	
  

	
  

37	
  

Chinese counterpart, depronominalisation only affected the pronoun him, where it wastranslated to 丈夫 zhàngfū “husband”.
Due to subtle language differences, word-for-word translation, if possible, maysometimes turn out unnatural. A good example is the use of pronouns in Japanese.
The Japanese pronoun for second person singular, あなた anata “you” is commonlyused by women to address their husbands or lovers. Likewise, the third person
singular pronouns 彼 kare “he” and 彼女 kanojo “she” are commonly used by
Japanese speakers to mean “boyfriend” and “girlfriend” respectively. Therefore, if thetranslator were to translate pronouns exactly as they were, it may appear unnatural to
Japanese speakers. In addition, unlike English and Chinese pronouns, Japanese
pronouns have many forms for each person, which seem to be correlated with gender,
dialectal differences and so on (Shibatani, 1990). In addition, the reference to persons
in Japanese is also subjected to sociocultural factors such as status differences. For
example, 俺 ore “I” is an informal term frequently used by men, and can be seen as
rude depending on the situation. It also emphasizes one’s status when used with those
who are younger or who have lower social status.
Another instance where word-for-word translation is awkward is the translation ofcolloquialisms that may appear in dialogues. For example, according to the online
Macmillan Dictionary, the word sick can be used to mean “very impressive, attractive,
enjoyable, etc.” such as in “The dress is sick! You look amazing!” A word-for-wordtranslation of this into Japanese and Chinese would give the following(24) Japanese: ドレス

は

病気

です

doresuwa

byouki

desu

dress

NOM

sick

COP

“(the) dress is sick”
Chinese: 这

件

衣服 是

生病

了

zhè

jiàn

yīfú

shēngbìng

le

DET

CL

dress to be

sick

le

“The dress is sick”

	
  

shì

	
  

38	
  

In (24), both the Japanese and Chinese translated sentences mean “The dress is sick.”
However, the colloquial meaning of sick does not exist in the Japanese and Chinese
languages. Therefore, the word-for-word translation in (24) would give a sentencethat is grammatically correct but makes no sense semantically to a native speaker ofJapanese or Chinese.
These language differences prove to be a challenge for machine translation systems.
A machine translation system can correctly translate a text word-for-word fromEnglish to Japanese or English to Chinese. However, certain connotations may end up
being lost in translation. While the translated text may be grammatically sound, theresulting meaning may be unnatural or makes no sense to the user. Likewise,translating words from Japanese or Chinese to English will also result in covert
information being lost.
We also observed interesting variations in the English-Chinese corpus where thetranslator used a different word to represent the same concept in different settings. For
example, for the word doctor, the translator consistently used 医生 yīshēng “doctor”
in the narratives and 大夫 dàifū “doctor” in dialogues between characters. Suchinformation will be useful for second language learners as they can see how one
concept can be represented in various ways depending on the context.
Many of the translation divergences discussed here are due to language differences. In
both corpora, most of these translation shifts are semantic mismatches. Nevertheless,translating styles can often be the cause of translation shifts. This data not only allows
users to see how a concept gets translated on a word level; it also allows users to see
how the underlying message of a sentence can be conveyed in a different way in thetarget language. This data can also be used to train tagging systems to automaticallytag for word-sense and link concepts based on the features in this study. This willallow researchers to save on time and human resources. Furthermore, to improve theefficiency of machine translation, the data can be used to train machine translation
systems to identify word environment and context in addition to WSD. With
increased feedbacks from such data, machine translation systems may overcome thechallenge of lexical divergences and produce more human-like translations.

	
  

	
  

39	
  

During the data tagging process, we came across a lot of concepts without synsets inthe WordNets. This lack of representation resulted in a lot of concepts that could not
be linked. Many of these concepts include idiomatic expressions and also lexicalized
multiword expressions. One of the challenges that WordNets has to face would be
how to best represent these expressions in the systems and link them to the existing
structures.
Also, one of the issues with the JWN is that certain lemmas are only represented inkanji characters in the synsets. As some concepts in the corpus were represented inhiragana, the senses for these concepts could not be found automatically. We noted
these concepts and commented that the lemmas should be added into the existing
synsets. In addition, the CWN also contains several POS errors. As we have noted
these errors, we hope to bring it to the attention of the maintainers of the CWN inorder to correct these and hence, improve the system.

	
  

	
  

40	
  
CHAPTER 6

CONCLUSION
In this study, we have used a quantitative and qualitative approach in studying
translation shifts in parallel corpora in English, Japanese and Chinese. Our results
show that a significant of divergences has occurred during the translation of the texts.
We have also attempted to describe some of these translation divergences. Although it
is challenging to establish the exact number of shifts due to language differences ortranslating style, we gauge that more than half of the translation shifts observed in thisstudy are definitely due to the differences in target language systems. Future studies
can work with texts from different genres such as news articles or contemporary
novels. Preliminary work has been done on another text – The Cathedral and the
Bazaar. We predict that the amount and types of translation shift will differ dependingon the genre of the text.
We would also like to point out that the Japanese and Chinese WordNet are not yet as
fully developed as the Princeton WordNet, which may have compromised the results,
as many things could not be linked. Through this study, we hope to have been able tohelp improve the WordNets in terms of coverage and also in correcting errors.
This parallel tri-text is the first such data annotated at this level of granularity for
English, Japanese and Chinese. The data from this study will be released so as toallow other scholars to work on it. It can also help second language learners ofJapanese and Chinese to understand that a concept in English can be represented invarious ways depending on the context. In addition, information from the data canalso be used to train machine translation systems in order to produce a more humanlike translation.

	
  

	
  

41	
  

References
Ahrenberg, L. (2007). Lines: An English-Swedish Parallel Treebank. In Proceedingsof the 16th Nordic Conference of Computational Linguistics (NODALIDA ‘07),
pp. 270–274. Tartu, Estonia.
Baker, M. (1996). Corpus-based translation studies: The challenges that lie ahead. In
H. Somers, (Ed.), Terminology, LSP and Translation: Studies in Language
Engineering in Honor of Juan C. Sager, pp. 175-186. John Benjamins
Baker, M., Fillmore, C.J., & Lowe, J.B. (1998). The Berkeley framenet project. In
Proceedings of the 36th Annual Meeting of the Association for Computational
Linguistics and 17th International Conference on Computational Linguistics –Volume 1, pp. 86-90. Montreal, Canada
Bentivogli, L., & Pianta, E. (2000). Looking for lexical gaps. In Proceedings of the 9th
EURALEX International Congress, pp. 8-12. Stuttgart, Germany.
Bentivogli, L., Forner, P. & Pianta, E. (2004). Evaluating cross-language annotationtransfer in the MultiSemCor corpus. In Proceedings of the 20th International
Conference on Computational Linguistics, pp. 364-370. Geneva.
Bloch, B. (1946). Studies in Colloquial Japanese II: Syntax. Language, 22(3), pp 200248.
Bond, F., Isahara, H., Fujita, S., Uchimoto, K., Kuribayashi, T., & Kanzaki, K. (2009).
Enhancing the Japanese WordNet. In Proceedings of the 7th Workshop on Asian
Language Resources, pp. 1-8. Singapore.
Brown, V., Mendes, E., & Natali, G. (1995). False friends and bugs and bugbears.
Zanichelli, Bologna.
Catford, J.C. (1965). A Linguistic Theory of Translation: An Essay in Applied
Linguistics. (Volume 8). Oxford University Press.
Čulo, O., Hansen-Schirra, S., Neumann, S., & Vela, M. (2008). Empirical studies on
language contrast using the English-German comparable and parallel CroCo
corpus. In Proceedings of “Building and Using Comparable Corpora”, LREC
2008 Workshop, Volume 31, pp. 47-51. Marrakesh, Morocco.
Cyrus, L. (2006). Building a resource for studying translation shifts. In Proceedingsof LREC 2006-Second International Conference on Language Resources and
Evaluation, Genoa, Italy.
Denny, J. P. (1986). The semantic role of noun classifiers. In Craig, C. (Ed.), Nounclasses and categorization, pp. 297–308. Philadelphia: John Benjamins
	
  

	
  

42	
  

Dorr, B.J. (1993). Machine Translation: A View from the Lexicon. MIT Press,
Cambridge, Massachusetts.
Fellbaum, C, editor. (1998). WordNet: An Electronic Lexical Database. MIT Press,
Cambridge, Massachusetts.
Fellbaum, C. (2005). WordNet and wordnets. In Brown, K. et al. (eds), Encyclopediaof Language and Linguistics (2nd edition.), pp. 665-670. Oxford: Elsevier
Ide, N. & Véronis, J. (1998). Introduction to the special issue on word sense
disambiguation: The state of the art. Computational Linguistics, 24(1), pp. 1-40.
Isahara, H., Bond, F., Uchimoto, K., Utiyama, M. & Kanzaki, K. (2008).
Development of the Japanese WordNet. In Proceedings of the 6th Conference on
Language Resources and Evaluation (LREC 2008), Marrakesh, Morocco.
Koehn, P. (2002). Europarl: A multilingual corpus for evaluation of machinetranslation. In MT Summit, Volume 5.
Liddy, E.D. (2001). Natural Language Processing. In Encyclopedia of Library andInformation Science, (2nd edition). New York: Marcel Decker, Inc.
Lo Cascio, V., Boraschi, P., & Corda, A. (1995). Correspondence between senses andtranslation equivalents: automatic reversal of a bilingual dictionary. In
Translation and Meaning. Part 3 of the Proceedings of the 2nd International
Maastricht-Lodz Duo Colloquium on ‘Translation and Meaning’, pp. 221-231.
Maastricht.
Marello, C. (1989). Dizionari bilingui: con schede sui dizionari italiani per francese,
inglese, spagnolo, tedesco (Volume 6). Zanichelli, Bologna.
Maynard, S.K. (1993). An Introduction to Japanese Grammar and Communication
Strategies, (4th edition). The Japan Times.
McCarthy, D. (2009). Word sense disambiguation: An overview. Language andLinguistics Compass, 3(2), pp. 537-558.
Mok, S.W.H. (2012). Issues and guidelines for tagging a multitext with WordNet.
NICT Internship report (Summer 2012).
Padó, S. & Erk, K. (2010). Translation shifts and frame-semantic mismatches: A
corpus analysis. International Journal of Corpus Linguistics. To appear.
Palmer, M. and Wu, Z. (1995). Verb Semantics for English-Chinese Translation.
Machine Translation, 9(4).

	
  

	
  

43	
  

Tan, L & Bond, F. (2011). Building and annotating the linguistically diverse NTUMC (NTU multilingual corpus). In Proceedings of the 25th Pacific Asia
Conference on Language, Information and Computation (PACLIC 25), pp. 367–
376. Singapore. ISBN 978-4-905166-02-3.
Shibatani, M. (1990). The Languages of Japan. Cambridge University Press. ISBN 0521-36918-5.
Teich, E. (2003). Cross-linguistic variation in system and text. A methodology for theinvestigation of translations and comparable texts (Volume 5). Walter de
Gryuter.
Vinay, J., & Darbelnet, J. (1995). Comparative Stylistics of French and English: A
Methodology for Translation. (J.C. Sager & M. Hamel, Trans.). Amsterdam andPhiladelphia, PA: John Benjamins. (Original work publish 1958).
Volk, M., Göhring, A., Marek, T., and Samuelsson, Y. (2010). SMULTRON (Version3.0) – The Stockholm MULtilingual parallel TReebank. An English-FrenchGerman-Spanish-Swedish parallel treebank with sub-sentential alignments.
Wilks, Y., & Stevenson, M. (1996). The grammar of sense: Is word-sense tagging
much more than part-of-speech tagging?. Technical Report CS-96-05,
University of Sheffield, Sheffield, U.K.
Xu, R., Gao, Z., Qu, Y., & Huang Z. (2008). An integrated approach for automaticconstruction of bilingual Chinese-English WordNet. The Semantic Web, pp.
302-314.
Sick.

(n.d.).

In

Macmillan

Dictionary

online.

http://www.macmillandictionary.com/dictionary/british/sick	
  Retrievedfrom