U.S. patent application number 12/722470 was filed with the patent office on 2010-07-08 for empirical methods for splitting compound words with application to machine translation.
Invention is credited to Philipp Koehn.
Application Number | 20100174524 12/722470 |
Document ID | / |
Family ID | 42312262 |
Filed Date | 2010-07-08 |
United States Patent
Application |
20100174524 |
Kind Code |
A1 |
Koehn; Philipp |
July 8, 2010 |
Empirical Methods for Splitting Compound Words with Application to
Machine Translation
Abstract
A statistical machine translation (MT) system may include a
compound splitting module to split compounded words for more
accurate translation. The compound splitting module select a best
split for translation by the MT system.
Inventors: |
Koehn; Philipp; (Cambridge,
MA) |
Correspondence
Address: |
CARR & FERRELL LLP
2200 GENG ROAD
PALO ALTO
CA
94303
US
|
Family ID: |
42312262 |
Appl. No.: |
12/722470 |
Filed: |
March 11, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10884174 |
Jul 2, 2004 |
7711545 |
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12722470 |
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Current U.S.
Class: |
704/4 |
Current CPC
Class: |
G06F 40/44 20200101;
G06F 40/268 20200101 |
Class at
Publication: |
704/4 |
International
Class: |
G06F 17/28 20060101
G06F017/28 |
Goverment Interests
ORIGIN OF INVENTION
[0002] The research and development described in this application
were supported by DARPA under grant number N66001-00-1-8914. The
U.S. Government may have certain rights in the claimed inventions.
Claims
1. A method comprising: identifying one or more split options for a
compounded word in a source language, each split option having a
translation in a target language; ranking the compounded word and
the one or more split options; and selecting a translation option
from the compounded word and the one or more split options.
2. The method of claim 1, further comprising: providing the
translation option to a machine translation system for translation
into the target language.
3. The method of claim 1, wherein said ranking comprises: ranking
the compounded word and the one or more split options based on the
number of split options.
4. The method of claim 1, wherein said ranking comprises: ranking
the compounded word and the one or more split options based on the
frequency of occurrence of the compounded word and the one or more
split options in a source language corpus.
5. The method of claim 1, wherein said ranking comprises:
identifying a translation pair including the compounded word in a
parallel corpus, said translation pair including a translation of
the compounded word in the target language; and comparing the
compounded word and the one or more split options to the
translation of the compounded word in the target language.
6. The method of claim 1, wherein said identifying comprises:
excluding a potential split option based on a part-of-speech of
said potential split option.
7. The method of claim 6, wherein the part-of-speech comprises one
of a preposition and a determiner.
8. An apparatus comprising: a split generator to identify one or
more split options for a compounded word in a source language, each
split option having a translation in a target language; a module to
generate ranking information for the compounded word and the one or
more split options; and a split selector to rank the compounded
word and the one or more split options based on the ranking
information and select a translation option from the compounded
word and the one or more split options.
9. The apparatus of claim 8, wherein the module comprises: a
frequency module to identify the frequency of occurrence of the
compounded word and the one or more split options in a source
language corpus.
10. The apparatus of claim 8, wherein the module comprises: a
translation lexicon to identify a translation pair including the
compounded word in a parallel corpus, said translation pair
including a translation of the compounded word in the target
language, and compare the compounded word and the one or more split
options to the translation of the compounded word in the target
language.
11. The apparatus of claim 8, wherein the module comprises a
translation table generated by splitting compounded words in a
parallel corpus and aligning the split compounded words with
corresponding target words in the parallel corpus.
12. The apparatus of claim 8, wherein the module comprises: a
module to exclude a potential split option based on a
part-of-speech of said potential split option.
13. The apparatus of claim 12, wherein the part-of-speech comprises
one of a preposition and a determiner.
14. An article comprising a machine-readable medium including
machine-executable instructions, the instructions operative to
cause a machine to: identify one or more split options for a
compounded word in a source language, each split option having a
translation in a target language; rank the compounded word and the
one or more split options; and select a translation option from the
compounded word and the one or more split options.
15. The article of claim 14, further comprising instructions to
cause the machine to: provide the translation option to a machine
translation system for translation into the target language.
16. The article of claim 14, wherein the instructions for ranking
comprise instructions to cause the machine to: rank the compounded
word and the one or more split options based on the number of split
options.
17. The article of claim 14, wherein the instructions for ranking
comprise instructions to cause the machine to: rank the compounded
word and the one or more split options based on the frequency of
occurrence of the compounded word and the one or more split options
in a source language corpus.
18. The article of claim 14, wherein the instructions for ranking
comprise instructions to cause the machine to: identify a
translation pair including the compounded word in a parallel
corpus, said translation pair including a translation of the
compounded word in the target language; and compare the compounded
word and the one or more split options to the translation of the
compounded word in the target language.
19. The article of claim 14, wherein the instructions for
identifying comprise instructions to cause the machine to: exclude
a potential split option based on a part-of-speech of said
potential split option.
20. The article of claim 19, wherein the part-of-speech comprises
one of a preposition and a determiner.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application Ser. No. 60/484,812, filed on Jul. 2, 2003, the
disclosure of which is incorporated here by reference in its
entirety.
BACKGROUND
[0003] Machine translation (MT) is the automatic translation from a
first language (a "source" language) into another language (a
"target" language). Systems that perform an MT process are said to
"decode" the source language into the target language.
[0004] A statistical MT system that translates foreign language
sentences, e.g., French, into English may include the following
components: a language model that assigns a probability P(e) to any
English string; a translation model that assigns a probability
P(f|e) to any pair of English and French strings; and a decoder.
The decoder may take a previously unseen sentence f and try to find
the e that maximizes P(elf), or equivalently maximizes
P(e)*P(f|e).
[0005] Compounded words may present a challenge for MT systems.
Compounding of words is common in a number of languages (e.g.,
German, Dutch, Finnish, and Greek). An example of a compounded word
is the German word "Aktionsplan", which was created by joining the
words "Aktion" and "Plan". Words may be joined freely in such
languages, which may greatly increase the vocabulary size of such
languages.
SUMMARY
[0006] A statistical machine translation (MT) system may include a
compound splitting module to split compounded words ("compounds")
for more accurate translation. The compound splitting module select
a best split for translation by the MT system.
[0007] The compound splitting module may identify split option(s)
for a compound, rank the compounds, and then pick a best
translation from the compound and split option(s). The compound
splitting module may rank using different metrics, e.g., frequency
of a split's parts in a corpus or translations of the compound in a
translation lexicon. The compound splitting module may exclude
split options based on parts-of-speech they contain, e.g.,
prepositions and determiners.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram of a statistical machine
translation (MT) system including a compound splitting module
according to an embodiment.
[0009] FIG. 2 is a block diagram of a compound splitting
module.
[0010] FIG. 3 is a flowchart describing a compound splitting
operation.
[0011] FIG. 4 shows splitting options for the compounded German
word "Aktionsplan".
[0012] FIG. 5 shows correspondences between the splitting options
for "Aktionsplan" and the English translation.
DETAILED DESCRIPTION
[0013] FIG. 1 illustrates a statistical machine translation (MT)
system 100 according to an embodiment. The MT system 100 may be
used to translate from a source language (e.g., French) to a target
language (e.g., English). The MT system 100 may include a language
model 105, a translation model 110, and a decoder 115.
[0014] The MT system 100 may be based on a source-channel model.
The language model 105 (or "source") may assign a probability P(e)
to any given English sentence e. The language model 105 may be an
n-gram model trained by a large monolingual corpus to determine the
probability of a word sequence. The translation model 110 may be
used to determine the probability of correctness for a translation,
e.g., the probability P(f|e) of a French string f given an English
string e. The parameter values for computing P(f|e) may be learned
from a parallel corpus including bilingual sentence pairs. The
translation model 110 may be, for example, an IBM translation model
4, described in U.S. Pat. No. 5,477,451. The decoder may be used to
identify the best translation by maximizing the product of
P(e)*P(f|e).
[0015] Compounding of words is common in a number of languages
(e.g., German, Dutch, Finnish, and Greek). The compounded words (or
"compounds") may greatly increase the vocabulary size of such
languages, which may present a challenge for MT systems.
[0016] In an embodiment, the MT system 100 may include a compound
splitting module 120 to determine if and how a compounded word
should be split in a translation operation. FIG. 2 shows various
components of the compound splitting module 120. These components
may include a split generator 205, a frequency module 210, a
primary translation lexicon 215, a secondary translation lexicon
220, a part-of-speech (POS) module 225, and a split selector
230.
[0017] FIG. 3 is a flowchart describing operations that may be
performed by the compound splitting module in an MT system for
translating German sentences into English. The split generator 205
may split a German word into possible split options (or "splits")
(block 305), e.g., into parts that have individual translations
into English words. The frequency module 210 may select split(s)
based on the frequencies of the splits' parts in the corpus (block
310). The primary translation 215 lexicon may check if the splits
have corresponding translations in the English translation of the
sentence (block 315), and the secondary translation lexicon 320 may
be used to account for special cases (block 320). The POS module
325 may qualify the splits based on statistics of parts-of-speech
in the translation lexicon (block 325). The split selector 230 may
then select the best split (block 330).
[0018] The split generator 205 may use known words, e.g., words
existing in a training corpus 150 (FIG. 1) to identify possible
splittings of a compound. In an experiment, the training corpus
used was Europarl, which is derived from the European parliament
proceedings and consists of 20 million words of German (available
at http://www.isi.edu/publications/europarl/). To speed up word
matching, the known words may be stored in a hash table based on
the first three letters. The known words in the hash table may be
limited to words having at least three letters.
[0019] The split generator 205 may account for filler letters
between words in the compound. For example, the letter "s" is a
filler letter in "Aktionsplan", which is a compound of the words
"Aktion" and "Plan". The filler letters "s" and "es" may be allowed
when splitting German words, which covers most cases. The splits
may be generated using an exhaustive recursive search. As shown in
FIG. 4, the split generator may generate the following splits for
"Aktionsplan": "aktionsplan"; "aktion-plan"; "aktions-plan"; and
"akt-ion-plan". Each part of the splits (i.e., "aktionsplan",
"aktions", "aktion", "akt", "ion", and "plan") exist as whole words
in the training corpus.
[0020] The frequency module 210 may identify the split having a
highest probability based on word frequency. Given the count of
words in the corpus, the frequency module may select the split S
with the highest geometric mean of word frequencies of its parts
p.sub.i (n being the number of parts):
arg max S ( p i .di-elect cons. S count ( p i ) ) 1 n
##EQU00001##
[0021] The frequency module 210 utilizes a metric based on word
frequency. The metric is based on the assumption that the more
frequent a word occurs in a training corpus, the larger the
statistical basis to estimate translation probabilities, and the
more likely the correct translation probability distribution will
be learned. However, since this metric is defined purely in terms
of German word frequencies, there is not necessarily a relationship
between the selected option and correspondence to English words. If
a compound occurs more frequently in the text than its parts, this
metric would leave the compound unbroken, even if it is translated
in parts into English. In fact, this is the case for the example
"Aktionsplan". As shown in Table 1, the mean score for the unbroken
compound (852) is higher than the preferred choice (825.6).
TABLE-US-00001 TABLE 1 Frequency of parts Mean score aktionsplan
(852) 852 aktion (960), plan (710) 825.6 aktions (5), plan (710)
59.6 akt (224), ion (1), plan (710) 54.2
[0022] On the other hand, a word that has a simple one-to-one
correspondence to English may be broken into parts that bear little
relation to its meaning. For example, the German word "Freitag"
(English: "Friday") may be broken into "frei" (English: "free") and
"Tag" (English: "day"), as shown in Table 2.
TABLE-US-00002 TABLE 2 Frequency of parts Mean score frei (885),
tag (1864) 1284.4 freitag (556) 556
[0023] The translation lexicons may be used to improve one-to-one
correspondence with English. The primary translation lexicon 215
can check for each split whether that split's parts have
translations in the English translation of the foreign language
sentence(s) in the parallel corpus containing the compound. In the
case of "Aktionsplan", the words "action" and "plan" would be
expected on the English side, as shown in FIG. 5. In case of
"Freitag" the words "free" and "day" would not be expected. This
information may be used by the compound splitting module 120 to
break up "Aktionsplan", but not "Freitag".
[0024] The primary translation lexicon 215 may be learned from the
parallel corpus 150. This can be done with the toolkit Giza, which
establishes word-alignments for the sentences in the two languages.
The toolkit Giza is described in Al-Onaizan et al., "Statistical
machine translation," Technical report, John Hopkins University
Summer Workshop (1999).
[0025] To deal with noise in the translation table, the primary
translation lexicon 215 may require that the translation
probability of the English word given the German word be at least
0.01. Also, each English word may be considered only once. If a
word is taken as evidence for correspondence to the first part of
the compound, that word is excluded as evidence for the other
parts. If multiple options match the English, the one(s) with the
most splits may be selected and word frequencies may be used as a
tie-breaker.
[0026] While this method has been found to work well for the
examples "Aktionsplan" and "Freitag", it failed in an experiment
for words such as "Grundrechte" (English: "basic rights"). This
word should be broken into the two parts "Grund" and "Rechte".
However, "Grund" translates usually as "reason" or "foundation".
But here, the more correct translation is the adjective "basic" or
"fundamental". Such a translation only occurs when "Grund" is used
as the first part of a compound.
[0027] The second translation lexicon 220 may be used to account
for such special cases. German words in the parallel corpus 150 may
be broken up with the frequency method. Then, the translation
lexicon may be trained using Giza from the parallel corpus with
split German and unchanged English. Since in this corpus "Grund" is
often broken off from a compound, the compound splitting module
learns the translation table entry "Grund" q "basic". By joining
the two translation lexicons, the same method may be applied, but
this time with the correct split of "Grundrechte".
[0028] A vast amount of splitting knowledge (for this data, 75,055
different words) is acquired by splitting all the words on the
German side of the parallel corpus. This knowledge contains for
instance that "Grundrechte" was split up 213 times and kept
together 17 times. When making splitting decisions for new texts,
the compound splitting module 120 may use the most frequent option
based on the splitting knowledge. If the word has not been seen
before, the compound splitting module may use the frequency method
as a back-off.
[0029] The POS module 225 may be used to prevent errors involving
the splitting off of prefixes and suffixes. For instance, the word
"folgenden" (English: "following") may be broken off into "folgen"
(English: "consequences") and den (English: "the"). This occurs
because the word "the" is commonly found in English sentences, and
therefore taken as evidence for the existence of a translation for
"den". Another example for this is the word "Voraussetzung"
(English: "condition"), which is split into "vor" and "aussetzung".
The word "vor" translates to many different prepositions, which
frequently occur in English.
[0030] To exclude these mistakes, the POS module 225 may only break
compounds into content words, e.g., nouns, adverbs, adjectives, and
verbs, and not prepositions or determiners. The German corpus may
be tagged with POS tags using a tagger, e.g., the TnT tagger, which
is described in Brants, T., "TnT--a statistical part-of-speech
tagger," Proceedings of the Sixth Applied Natural Language
Processing Conference ANLP (2000).
[0031] The POS module 225 may obtain statistics on the POS of words
in the corpus and use this information to exclude words based on
their POS as possible parts of compounds.
[0032] Experiments were performed using a corpus of 650,000 NP/PPs.
The corpus included an English translation for each German NP/PP.
The corpus was extracted from the Europarl corpus with the help of
a German and English statistical parser. This limitation was made
for computational reasons, since most compounds were expected to be
nouns. An evaluation of full sentences is expected to show similar
results.
[0033] The performance of the compound splitting module 120 was
evaluated on a blind test set of 1000 NP/PPs, which contained 3498
words. To test one-to-one correspondence of split or not-split
German words into parts that have a one-to-one translation
correspondence to English words, the test set was manually
annotated with correct splits. The splitting techniques were then
evaluated against this gold standard. The results of this
evaluation are given in Table 3.
TABLE-US-00003 TABLE 3 correct wrong metrics Method split not not
faulty split prec. recall acc. Raw 0 3296 202 0 0 -- 0.0% 94.2%
Eager 148 2901 3 51 397 24.8% 73.3% 87.1% Fre- 175 3176 19 8 122
57.4% 96.6% 95.7% quency based Parallel 180 3270 13 9 27 83.3%
89.1% 98.6% Parallel 182 3287 18 2 10 93.8% 90.1% 99.1% and POS
[0034] In the columns, "correct-split" refers to words that should
be split and were split correctly. "Correct-not" refers to words
that should not be split and were not split. "Wrong-not" refers to
words that should be split but Were not split. "Wrong-faulty"
refers to words that should be split, were split, but incorrectly
(either too much or too little). "Wrong-split" refers towards that
should not be split, but were split. "Precision" is the ratio of
(correct split)/(correct split+wrong faulty split+wrong superfluous
split). "Recall" is the ratio or (correct split)/(correct
split+wrong faulty split+wrong not split). "Accuracy" is the ratio
of (correct)/(correct+wrong).
[0035] In the rows, "raw" refers to the results with unprocessed
data with no splits. "Eager" refers to the biggest split, i.e., the
compound split into as many parts as possible. If multiple biggest
splits are possible, the one with the highest frequency score is
taken. In the "frequency based" method, the word is split into most
frequent words. In the "parallel" method, the split is guided by
splitting knowledge from a parallel corpus. In the combined
"parallel and POS" method the split is guided by splitting
knowledge from a parallel corpus with an additional restriction on
the POS of split parts.
[0036] For one-to-one correspondence, the most sophisticated method
that employs splitting knowledge from a parallel corpus and
information about POS tags provides the best results, with 99.1%
accuracy. The main remaining source of error is the lack of
training data. For instance, the method failed on more obscure
words such as "Passagier-aufkommen" (English: "passenger volume"),
where even some of the parts have not been seen in the training
corpus.
[0037] An experiment was performed to test translation quality with
a word-based MT system. The translation model used was the IBM
Model 4. The system was trained on the 650,000 NP/PPs with the Giza
toolkit, and the translation quality was evaluated on the same 1000
NP/PP test set as in experiment described above for one-to-one
correspondence. Training and testing data was split consistently in
the same way. The translation accuracy is measured against
reference translations using the BLEU score, described in Papineni
et al., "BLEU: a method for automatic evaluation of machine
translation," Proceedings of the 40th Annual Meeting of the
Association for Computational Linguistics (ACL) (2002). The results
are shown in Table 4.
TABLE-US-00004 TABLE 4 Method BLEU Raw 0.291 Eager 0.222 Frequency
based 0.317 Parallel 0.294 Parallel and POS 0.306
[0038] In this experiment, the frequency based method produced
better translation quality than the more accurate methods that take
advantage of knowledge obtained from the parallel corpus. One
reason for this may be that the system recovers more easily from
words that are split too much than from words that are not split up
sufficiently. However, this has limitations as shown by the poor
results of the eager method.
[0039] Compound words violate the bias for one-to-one word
correspondences of word based statistical MT systems. This is one
of the motivations for phrase based systems that translate groups
of words, such as that described in co-pending application Ser. No.
10/402,350, filed Mar. 27, 2003, which is incorporated herein in
its entirety. The results are shown in Table 5.
[0040] The translation quality was also tested using a phrase-based
MT system. This system was trained with the different flavors of
the training data, and the performance was evaluated as before.
TABLE-US-00005 TABLE 5 Method BLEU Raw 0.305 Eager 0.344 Frequency
based 0.342 Parallel 0.330 Parallel and POS 0.326
[0041] Here, the eager-splitting method that performed poorly with
the word-based statistical MT system gave the best results. The
task of deciding the granularity of good splits may be deferred to
the phrase-based statistical MT system, which uses a statistical
method to group phrases and rejoin split words. This turns out to
be even slightly better than the frequency based method.
[0042] In an embodiment, the words resulting from compound
splitting could also be marked as such, and not just treated as
regular words.
[0043] A number of embodiments have been described. Nevertheless,
it will be understood that various modifications may be made
without departing from the spirit and scope of the invention. For
example, blocks in the flowchart may be skipped or performed out of
order. Accordingly, other embodiments are within the scope of the
following claims.
* * * * *
References