will be the future of MT (machine translation)?
How will it affect human translators? During
the 1950s and early 1960s, we heard that MT
would soon replace human translation, but
it did not. The ALPAC report (1966) put a
damper on research in machine translation
world-wide for a number of years, but in the
early 1980s, people were again suggesting
that machines would soon replace human translators.
The 1990s were relatively calm, with modest
claims by the promoters of MT systems. Then,
more recently, in the early years of the 21st
century, developers of statistical machine-translation
systems are enthusiastically announcing, yet
again, that the quality of raw MT output will
soon meet or exceed the quality of human translation.
Is this just another false alarm, or is something
fundamentally different this time? In this
article, I will make claims about the future
of MT systems, the future of translation memory
(TM) systems, and the role of quality assurance
(QA) in the future of human translators.
Machine Translation (MT)
first prediction is that
traditional, hand-coded, ruled-based machine-
translation systems will receive less attention
in the next few years.
systems consist of three phases of processing:
analysis of the source text, transfer (to
accommodate differences between the source
and target languages), and generation of the
target text from an intermediate representation.
They require enormous amounts of human time
to develop the rules, and the quality of the
raw output is low unless the system has been
tailored to a very narrow domain and the source
text conforms to this domain.
systems are built on the following assumption
about the nature of language: that meaning
can be computed from the bottom up (that is,
starting from isolated, individual words,
and combining them into larger and larger
units). At first glance, this assumption seems
obviously true. How else could we figure out
the meaning of a sentence other than by combining
the meanings of individual words? However,
in fact, humans do not deal with words in
isolation when analyzing a text. Context is
continuously being taken into account, even
if we are not consciously aware of it. In
most rule-based machine-translation systems,
context is only brought in during word-sense
disambiguation. Once the sense of a word is
identified, it is assumed that the word-sense
can thereafter be treated without further
reference to context.
terms of linguistic theory, rule-based systems
are typically associated with some branch
of generative grammar, if there is even a
full syntactic analysis at all. There is an
extensive literature describing rule-based
systems and the linguistic models on which
they are based. See, for example, the references
in Hutchins (1986). Of course, the study of
syntax did not originate in the 1960s when
the generative approach began to dominate
the linguistic scene. As Chomsky himself points
out, his approach to syntax is not entirely
original and shares much with Cartesian philosophy.(Chomsky
contrast with rule-based systems, which can
now be called classic MT systems, there is
a substantially different approach called
statistical machine translation (SMT). While
rule-based MT systems can be viewed as being
based on grammars and dictionaries, which
have been around for thousands of years, statistical
machine translation systems, on the other
hand, are based on bilingual corpora. After
initial experiments in the 1990s (Brown et
al 1990) and then a period of little activity
in SMT, there has recently been a flurry of
activity in this area. In statistical machine
translation, the starting point is an extensive
collection of pairs of documents. Each pair,
often called a bitext, is a source text and
a target text. The target text is normally
a human translation of the source text. Each
bitext is segmented, usually at the sentence
or paragraph level, and corresponding segments
in the source text and target texts are linked.
Then the source text is fully indexed for
rapid retrieval of segments containing a particular
word or phrase, along with the corresponding
segments of target text, which presumably
contain the translation of the word or phrase
in question. In addition, an extensive statistical
analysis of the corpus of bitexts results
in a table of correspondences between source
language words or phrases and target language
words or phrases. In a sense, this table can
be thought of as a bilingual dictionary that
has been automatically derived from the bitext
corpus. However, this does not mean that a
statistical machine-translation system is
equivalent to a rule-based system. Perhaps
the most significant difference, other than
the obvious difference of whether the bilingual
dictionary is created manually or automatically,
is that the machine translation in an SMT
system is not just a one-to-one mapping of
source-language words to target-language words.
An SMT system is not just a glorified word-for-word
dictionary lookup and substitution procedure.
Instead, context is taken into account by
matching chunks of source text with chunks
of target text whenever possible. This matching
is not done by applying a linguistic model
of language but rather by using statistical
methods that have proven very effective in
automatic speech recognition. In a purist
approach to SMT, there is a degree of disregard
for the classic linguistic levels (morphology,
syntax, and semantics). Is would seem logical
that morphological processing will eventually
be needed in SMT, especially for highly inflected
languages, in order to map between base forms
of words instead of treating each inflected
form separately. For example, the word "shoe"
should probably correspond to the same base
form in another language regardless of whether
that word is inflected one way as the subject
of a sentence and another way as the direct
object. Also, some differences in word order
that involve long distances dependences, such
as the placement of the verb at the end of
dependent clauses, will best be expressed
with some sort of syntactic representation.
future of statistical machine-translation
systems is probably a hybrid approach in which
morphology and syntax are somehow taken into
account. This may involve using some explicitly
rule-based components, such as a morphological
analyzer, or it may involve alternative approaches
to morphology, such as the use of analogical
models of language (AML). In AML, many exemplars,
such as specific inflected forms each paired
with the appropriate base form, along with
features and a distance metric, are the input
to the system, rather than hand-coded rules.
These hybrid systems will probably still be
focused on bilingual corpora rather than traditional
rules, and thus we will call them "data-driven"
systems as opposed to rule-based systems.
second prediction is that
whenever sufficient quantities of high-quality
bilingual corpora are available for the domain
being treated, data-driven machine-translation
systems will soon outperform classic rule-based
systems in quality of output, though probably
not in speed.
computing power becomes even less expensive,
the speed difference between rule-based and
data-driven systems will, of course, become
a less important factor. However, processor
speed cannot make up for a lack of a sufficiently
large and suitable bitext corpus.
Translation Memory (TM)
made some testable predictions concerning
the future of MT, let us turn to the second
part of the title of this article: TM (Translation
Memory). Traditional TM is sentence-level
and language independent. An unordered list
of translation units, each consisting of a
source-language segment and a target-language
segment, is indexed. Then a source text to
be translated is segmented and compared with
the TM database. Exact matches and "fuzzy"
matches (that is, source segments that partially
match against the source-language segment
in a translation unit), are displayed for
a human translator to accept as is, edit,
or reject. Source segments that do not result
in either an exact match or a fuzzy match
above a certain threshold of similarity, do
not result in any target text being displayed.
is the basic difference between an MT system
and a TM system. An MT system attempts to
produce a complete target text that can be
used in its raw form or after post-editing
by a human translator. A TM system, on the
other hand, generally does not produce a complete
translation but instead makes suggestions
to a human translator who is responsible for
producing a suitable target-language text.
If a sufficient number of retrieved translation
units are used by the human translator with
little or no editing, a TM system may result
in a much faster translation than a translation
"from scratch" in which every segment
of source text is translated by a human. Of
course, if only a small percentage of the
segments in a source text result in the retrieval
of a translation unit, the use of a TM system
may not significantly increase translation
is the direction of development of TM systems?
While traditional TM systems are highly effective
when translating a slight revision of a previously
translated document (for example, the documentation
for a new version of a product that involves
only minor changes or a revised version of
documentation that was translated before a
product was finalized), they are not very
effective in other contexts. For situations
where the percentage of "hits" (source
segments for which a usefully similar target
segment is retrieved) is rather low, other
TM tools are needed. On additional tool is
a subsegment-level lookup feature that searches
for portions of a segment, sometimes called
a "chunk", and displays all those
translation units that contain that chunk
of text. The translator examines those translation
units and decides whether they contain useful
challenge of subsegment-level lookup is that
there can be an overwhelming number of hits
to look through. Another is knowing which
chunks are going to be found in the database.
Looking up a chunk and retrieving no translation
units is a waste of time. One approach to
dealing with these challenges is to automatically
look up subsegment-level chunks and display
for the translator those chunks that were
found, ranking the target language units for
each chunk according to likely relevance,
for example, according to the number of words
surrounding the chunk that are found in both
the source segment and the target segment.
For inflected languages, the lookup of chunks
will be more effective if language-specific
morphological processing is performed on the
bilingual corpus to allow for matches when
the source-language chunk exists in the translation
memory but in a different inflected form.
Another trend in TM systems is toward the
the retention of the integrity of the source
and target texts as bitexts, rather than as
unordered sets of translation units in isolation.
Bitexts allow the translator to explore as
much context as desired surrounding both the
source chunk and the target chunk.
is no need to state a prediction that TM systems
are moving toward automated subsegment-level
lookup of chunks. This feature is already
available in several commercial systems.
third prediction is that
TM systems with automated subsegment-level
lookup will begin to offer morphological analysis
for some languages and that these system will
begin to exploit the existence of a bitext-oriented
translation memory by providing features that
cannot be provided when the translation memory
consists of unordered translation units in
Convergence and Quality Assurance
is probably obvious from the title of this
article that there will be a prediction involving
the convergence of MT and TM. It is not a
huge step from (1) a TM system that automatically
looks up and ranks chunks of text to (2) an
MT system that puts those chunks together
into a target-language sentence.
fourth prediction is that
there will be integrated systems using the
same bitext corpus that combine TM and MT
under the control of the translator.
a general level, this is actually a very old
prediction that dates back more than twenty
years. What is new is to specify that these
integrated systems will involve a convergence
of TM and MT using the same bitext corpus.
are the challenges for such integrated systems?
The major challenge is quality. It could become
easy for a translator to accept low-quality
target text sentences in the interest of efficiency.
It would go beyond the scope of this article
to discuss quality extensively. Instead, we
will introduce two questions: what is quality
and how important is it?
suggest that we use the definition of quality
found in the ASTM International standard F2575
on quality assurance in translation (ASTM
2006): degree of conformance to an agreed-upon
set of set of specifications. Immediately,
we can conclude that by this definition quality
is important, since it is defined in terms
of what all parties have agreed to be important
and have formalized in written specifications.
This is far from an absolute definition of
quality. Instead, it is a flexible definition
relative to a particular translation project.
Theoretically, this definition is consistent
with Functionalism in translation studies.
this flexibility, let us examine three specifications
that are commonly used: coherence, consistency,
and accuracy. A translation that has no textual
coherence is very difficult to read. A translation
that does not use key terms consistently is
likely to be confusing. And a translation
that is factually inaccurate or departs from
standard terms, even if the non-standard terms
are used consistently, is often unacceptable.
A perhaps astounding fact is that neither
TM nor MT guarantees these properties in a
translation. The larger the bitext corpus,
the more variety will be found in translation
units retrieved for a chunk of text. The hit
ranked highest by some mechanical procedure
that does not involve understanding real-world
context as well as surrounding text may not
be the best hit. Consistency is best managed
by terminology management. Most translator
tools already include terminology management,
so it does not make sense to predict that
it will be available. It is available. The
question is whether we will use it effectively.
Effective use of terminology management and
other tools involves constant awareness of
all aspects of the context of a translation.
There is no indication that MT and TM will
achieve this in the foreseeable future, but
humans are particularly good at it.
fifth and final prediction
is a bit scary: in the future, the only kind
of non-literary translator who will be in
demand is one who can craft coherent target
texts that, when appropriate, override the
blind suggestions of the computer.
is actually good news for translators: they
will be more human, rather than less. They
will be involved in the entire quality assurance
(QA) process of creating specifications appropriate
for the audience and purpose of a particular
translation and making sure they are adhered
to at every step of the project. If that sounds
like a project manager, so be it. The future
is ours: do we want to be viewed more as file
clerks afraid of being replaced by document
management systems or as confident professionals
who are gradually being freed from the drudgery
of detailed, mechanical text manipulation
so that they can focus on the bigger picture
of quality assurance in information management?
methodology for testing whether my predictions
are accurate is simple: wait a few years and
look at machine translation systems, translation
memory systems, and the profile of well-paid
human translators. I suspect that most of
my predictions will come to pass within five
to ten years. Let's get together again at
that point and see.
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