Systems and methods for identifying parallel documents and sentence fragments in multilingual document collections

Abstract

Systems, computer programs, and methods for identifying parallel documents and/or fragments in a bilingual collection are provided. The method for identifying parallel sub-sentential fragments in a bilingual collection comprises translating a source document from a bilingual collection. The method further includes querying a target library associated with the bilingual collection using the translated source document, and identifying one or more target documents based on the query. Subsequently, a source sentence associated with the source document is aligned to one or more target sentences associated with the one or more target documents. Finally, the method includes determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.

Description

BACKGROUND

1. Field of the Invention

The present invention relates generally to statistical machine translation of multilingual documents and more specifically to systems and methods for identifying parallel segments in multilingual document collections.

2. Description of the Related Art

In the field of statistical machine translation, large collections of training data are required to develop and implement systems and methods for translating documents. Training data comprises parallel segments which are documents or fragments that are literal, or parallel, translations of each other in two languages. Currently, there is a lack of sufficiently large parallel corpora for most language pairs. A language pair refers to the two languages used within the parallel corpora. Examples of language pairs include English-Romanian or English-Arabic.

Large volumes of material in many languages are produced daily, and in some instances, this material may comprise translational equivalents. For example, a news story posted on the World Wide Web (WWW) on an English-language website may be a translation of the same story posted on a Romanian-language website. The ability to identify these translations is important for generating large collections of parallel training data.

However, because news web pages published on a news website typically have the same structure. As such, structural properties, such as HTML structures, can not be used to identify parallel documents. Further, because web sites in different languages are often organized differently and a connection is not always maintained between translated versions of the same story, URLs of articles may be unreliable. Further, a news website may contain comparable segments of text that relate to the same news story, but the comparable segments or articles should not necessarily be identified as parallel documents. Comparable segments may be referred to as “noisy translations” of the sentences.

However, these comparable segments may include one or more parallel fragments that can be added to the training data even though the entire segment is not a parallel translation of a comparable segment. For example, a quote within a news article may be translated literally even though the rest of the document is merely related to a comparable segment in another language.

Current methods perform computations at a word level and do not distinguish parallel translations of documents from comparable documents. As such, these methods result in many false positives where a comparable document may be erroneously classified as a parallel translation.

SUMMARY

Systems, computer programs, and methods for identifying parallel documents and/or fragments in a bilingual collection are provided. The method for identifying parallel sub-sentential fragments in a bilingual collection comprises translating a source document from a bilingual collection. The method further includes querying a target library associated with the bilingual collection using the translated source document, and identifying one or more target documents based on the query. Subsequently, a source sentence associated with the source document is aligned to one or more target sentences associated with the one or more target documents. Finally, the method includes determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an illustration of an environment in which some embodiments may be practiced;

FIG. 2 depicts a block diagram illustrating an exemplary training set generator according to exemplary embodiments;

FIG. 3 depicts a block diagram illustrating an exemplary parallel document engine according to exemplary embodiments;

FIG. 4 depicts a block diagram illustrating an exemplary parallel fragment engine according to exemplary embodiments;

FIG. 5 depicts a flowchart illustrating an exemplary process for automatically generating parallel corpora from multi-lingual document sources according to exemplary embodiments;

FIG. 6 depicts a flowchart of an exemplary process for determining whether documents are parallel based on sentence alignments according to exemplary embodiments;

FIG. 7 depicts a flowchart of an exemplary process for extracting parallel sentence fragments from comparable corpora according to exemplary embodiments; and

FIG. 8 depicts a flowchart of an exemplary process for generating a fine lexicon according to exemplary embodiments.

DETAILED DESCRIPTION

A system and method for identifying parallel documents and/or fragments in a bilingual document collection is provided. The present method and system can be used with documents posted on the Internet without relying on properties such as page structure or URL. Further, the system and method is able to distinguish between parallel documents and comparable documents. The method and system may alternatively or additionally be used to extract parallel fragments from comparable corpora at the sub-sentential level to increase the amount of parallel data for statistical machine translation (SMT).

FIG. 1 depicts an illustration of an environment 100 in which some embodiments may be practiced. The environment 100 may comprise a document collection server 102 communicatively connected to a client 106 via a network 104. The document collection server 102 may comprise a memory 110 configured to store one or more bilingual document collections, one or more parallel documents, one or more parallel fragments, and/or one or more set of annotations indicating parallel data. The document collection server 102 may comprise a processor configured to access the memory 110 and process instructions. The document collection server 102 may communicate over the network 104 using a communications interface. The network 104 may comprise a public network (e.g., the Internet) or a private network. The client 106 may include storage, a display, or additional functionality beyond the scope of the embodiments.

The client 106 may comprise a computational device such as a personal computer. The client 106 may include a training set generator 108. The training set generator 108 may comprise hardware, software, or firmware and may be configured to identify parallel documents and/or fragments in bilingual collections. The training set generator 108 may be configured to access bilingual collections stored in the document collection server 102. Further, the training set generator 108 may transmit data indicating parallel documents or fragments to the document collection server 102.

FIG. 2 depicts a block diagram illustrating an exemplary training set generator, such as the training set generator 108 of FIG. 1, according to exemplary embodiments. The training set generator 108 may identify parallel documents and/or fragments within bilingual document collections. The bilingual document collections may comprise documents collected from the Internet or the World Wide Web (WWW) in exemplary embodiments. The training set generator 108 may comprise a word translator engine 202, a query engine 204, a document selector engine 206, a parallel document engine 208, and a parallel fragment engine 210. Because sentence alignment requires a large amount of computations, the word translator engine 202, and the query engine 204 may be configured to reduce the number of documents with which sentence alignment is performed.

The word translator engine 202 is configured to translate each word within a source document to generate a translated source document. A source document is a document in the bilingual document collection of which a parallel translation is sought. The source document, for example, may comprise a news article written in English. For example, the training set generator 108 may be configured to determine whether any documents written in Romanian associated with a target library are parallel translations of, or contain parallel translations of fragments of, the source document.

A target library comprises one or more target documents. Target documents may include documents or segments that possibly comprise a parallel translation of the source document. Target documents may be obtained from sources such as the Internet or other known libraries and may be classified according to a classifier such as date, URL, or the like. The word translator engine 202 translates the source document word-by-word or literally into the language of the target library. To further the example, the news article written in English is translated word-by-word into Romanian, which may be utilized as the language of the target library.

The query engine 204 is configured to query the target library using the translated source document. The query engine 204 selects a subset comprising the documents that are most similar to the translated source document. The subset may be limited to the top N documents. In some embodiments, N may be a constant such as the number twenty (20).

The document selector engine 206 aligns one or more target sentences in each of the target documents in the subset to one or more sentences in the source document. By aligning the sentences, the training set generator 108 avoids falsely classifying documents as parallel based only on word-level comparisons. Based on the sentence alignments, the document selector engine 206 may discard one or more of the top N documents that, while comprising word-level translations, do not comprise sentence-level translations. For example, the document selector engine 206 may discard documents that do not have a specified number of sentences that can be aligned. In other embodiments, the document selector engine 206 may discard sentence pairs within the aligned documents that do not contain a specified number or percentage of words that are translations of each other. By aligning sentences within the source documents and the target documents, the training set generator 108 can distinguish parallel documents from comparable documents.

The parallel document engine 208 is configured to determine whether a document pair comprising the source document and the target document is a parallel translation. The parallel document engine 208 is discussed further with respect to FIG. 3 and FIG. 6.

The parallel fragment engine 210 is configured to identify parallel sentence fragments in non-parallel document pairs such as document pairs from news sources on the Internet. The parallel fragment engine 210 is discussed further in FIG. 4, FIG. 8, and FIG. 9. Although FIG. 2 shows various engines comprising the training set generator 108, fewer or more engines may comprise the training set generator 108 and still fall within the scope of the various embodiments.

FIG. 3 depicts a block diagram illustrating an exemplary parallel document engine 208 according to exemplary embodiments. The parallel document engine 208 determines whether the target document is the parallel translation of the source document. The parallel document engine 208 comprises a sentence analysis module 302 and a document classification module 304.

The sentence analysis module 302 determines whether aligned sentences based on the sentence alignments generated by the document selector engine 206 are translations of each other independently of the context within the document or segment. The sentence analysis module 302 inspects the sentence pairs between the sentences in the source document and the sentences in each of the documents in the subset comprising the documents that are most similar to the translated source document to compute sentence-level links. The sentence analysis module 302 distinguishes between parallel and non-parallel sentence pairs based on sentence features that can be extracted from a word-level alignment of the two aligned sentences. The sentence features are identified by linking each word in the sentences with its best translation candidate in the paired sentence. This linking process may be referred to as “greedily linking.”

Features that can be extracted by greedily linking include, for example, the percentage of words that are aligned, contiguous phrases that are aligned, and contiguous phrases that are not aligned. After the features have been extracted, a Maximum Entropy-based parallel sentence classifier (not shown) may be trained to obtain positive and negative classifiers for the sentences. In exemplary embodiments, the following Maximum Entropy probability equation may be used: $P\left(c_i|\mathrm{sp}\right)=\frac{1}{Z\left(\mathrm{sp}\right)}\prod _{j=1}^k{\lambda }_j^{f_{\mathrm{ij}}\left(c,\mathrm{sp}\right)}$ where c_i is the class (c₀=“parallel”, c₁=“not parallel”), sp is the percentage of words in a sentence pair that have a translation in the other sentence of the sentence pair. Z(sp) is a normalization factor, and ƒ_ij are the feature functions (indexed both by class and by feature). A feature function, ƒ(sp), is defined as the value of the word overlap of the sentences in the sentence pair sp. The resulting model has free parameters λ_j, the feature weights. The parameter values that maximize the likelihood of a given training corpus can be computed using known optimization algorithms. Based on the positive and negative classifiers, sentence level links between the source document and the documents within the subset are obtained.

The document classification module 304 is configured to select the document within the subset that shares the most sentence level links with the source document. The document within the subset that shares the most sentence-level links with the source document is referred to as the target document. The document classification module 304 then determines whether the target document is a parallel translation of the source document by comparing the length of the target document to the length of the source document and determining whether a noisy sentence threshold and a monotone sentence threshold are met. This process is described in greater detail in association with FIG. 6. The output of the document classification module 304 may comprise an annotation indicating that the target document is a parallel translation of the source document.

FIG. 4 depicts a block diagram illustrating an exemplary parallel fragment engine 210 according to exemplary embodiments. The parallel fragment engine 210 may be configured to extract parallel sentence fragments from within non-parallel corpora. Thus, the parallel fragment engine 210 is configured to determine whether a sentence fragment in the source document is a parallel translation of a sentence fragment in the target document. For example, if the document classification module 304 determines that the source document and the target document are non-parallel, that is the target document is not a parallel translation of the source document, the documents are likely to be comparable documents. Comparable documents may also be referred to as “noisy translations.” The comparable documents may comprise sub-sentential parallel fragments that may be extracted by the parallel fragment engine 210 and added to a training data set. The parallel fragment engine 210 may comprise a candidate selection filter 402, a coarse lexicon 404, a probability module 406, a fine lexicon 408, and a fragment detection module 410.

The candidate selection filter 402 discards sentence pairs in the subset comprising the segments or documents selected by the document selection engine 206. The candidate selection filter 402 discards sentence pairs that have very few words that are translations of each other based on the coarse lexicon 404. For example, sentence pairs that comprise three or fewer words that appear in both the source sentence and the target sentence may be discarded. In other embodiments, sentence pairs may be discarded according to a percentage of words appearing in both the source sentence and the target sentence.

The coarse lexicon 404 comprises a probabilistic lexicon derived from an initial parallel corpus such as a training data set. The coarse lexicon 404 may be obtained by running a GIZA++ implementation of the IBM word alignment models on the initial parallel corpus. In the coarse lexicon, each source word may be associated with many possible translations. For example, in some embodiments, each source word is associated with, on average, twelve possible translations. Each of these possible translations may be further associated with a probability. The coarse lexicon is used to retain most of the existing comparable sentences occurring between the source document and the target document.

The probability module 406 is configured to greedily link each word in the source sentence to the best translation occurring in the target sentence. Using the fine lexicon 408, the probability module 406 then assigns values to each word alignment indicating the probability that the source word is properly aligned with, or a translation of, the target word. The probability module 406 may filter these values further. The fine lexicon 408 may be generated in exemplary embodiments according to the process discussed in connection with FIG. 8.

The fragment detection module 410 may be configured to detect a parallel sentence fragment based on the values assigned to each word alignment based on the fine lexicon 408. In some embodiments, the fragment detection module 410 may further filter the fragment matches. This process is discussed in greater detail in connection with FIG. 7. The output of the parallel fragment engine 210 comprises at least one parallel fragment that may be added to the training set.

FIG. 5 depicts a flowchart illustrating an exemplary process 500 for automatically generating parallel corpora from bilingual document sources according to exemplary embodiments. The process 500 identifies parallel segments and fragments in the bilingual documents according to sentence pairs identified between the documents. The process 500 may be used to generate a training set for statistical machine translation (SMT). The process 500 comprises accessing a bilingual document collection, translating words in a source document, querying a target library using the translated source document, selecting a subset comprising a target document from the target library, aligning the sentences in the source document with sentences in the target document, and determining whether the source document is a parallel translation of the target document. The method may alternatively or additionally include determining whether a fragment in the source document is parallel to a fragment in the target document. If the source document or a fragment thereof is a parallel translation of the target document, the parallel portions may be stored within a training set.

At step 502, each word in the source document is independently translated into the language of the target document to generate a translated source document. The words may be translated according to a dictionary, for example.

At step 504, the translated source document is run as a query against a large library comprising documents written in the target language. In step 506, the source document is paired with a subset comprising the most similar documents written in the target language according to the results of the query. In some embodiments, the subset may be limited to a constant number, N, of the most similar documents. N may be any number up to and including the number of documents in the target library. N may be limited by a computational capacity of a computer such as client 106.

At step 508, parallel sentence pairs between the source document and the documents in the subset are identified. The sentence pairs may be identified by the sentence analysis module 302.

After the sentence pairs are computed in step 508, step 510 is performed to determine whether the source document and at least one of the documents in the subset are parallel translations. In some embodiments, the parallel document engine 208 may perform this determination. The process used in step 510 is discussed in greater detail in connection with FIG. 6. If the documents are parallel, the parallel documents are added to the training set in step 512. In other embodiments, step 510 may not be performed and the process 500 proceeds to step 514 without determining whether any documents are parallel translations of each other.

If the documents are not parallel, step 514 is performed to determine whether there are parallel fragments in the paired documents. In some embodiments, the parallel fragment engine 210 may perform this determination. The process used in step 514 is discussed in greater detail in connection with FIG. 7 and FIG. 8. If there are parallel fragments in the sentences, the parallel fragments are added to the training set in step 516. In other embodiments, the process 500 may not perform step 514 and may instead terminate after performing steps 510 and 512.

FIG. 6 depicts a flowchart of a process 600 for determining whether a source document is a parallel translation of a document in the subset based on sentence alignments, according to exemplary embodiments. The process 600 may be performed as step 510 in process 500. In some embodiments, the process 600 may be performed by the parallel documents engine 208.

In step 602, a noisy sentence pair threshold is determined. A noisy sentence pair is a sentence pair comprising a first sentence that is at least a rough translation of one or more second sentences. For example, the first sentence may include the words, in a first language, “Mary went to the park with her dog,” while the second sentence may include the words, in a second language, “Mary went to the park.” In some embodiments, the noisy sentence threshold may be a percentage of the sentences in the source document. The noisy sentence threshold, for example, may be thirty percent (30%) of the sentences in the document.

At step 604, a monotone sentence pair threshold is determined. A monotone sentence pair is a sentence pair comprising words that have a high probability of being a parallel translation of one another. In exemplary embodiments, the monotone sentence pair may be a percentage of the noisy sentences. For example, the monotone sentence pair threshold may be ninety percent (90%) of the noisy sentence pairs. The noisy sentence pair threshold and the monotone sentence pair may be determined initially and/or later adjusted according to the accuracy of the identification of the parallel documents.

At step 606, the target document in the subset selected in step 506 having the most sentence pairs aligned with the source document according to step 508 is selected. After step 606, a series of determinations is made to determine whether the source document is a parallel translation of the target document. If the outcome of any of these determinations is negative, the source document is not determined to be parallel to the target document.

At step 608, comprises determining whether the length of the source document is approximately equal to the length of the target document is determined. Step 608 may be performed by the document classification module 304. This determination may be made according to the number of sentences present in the target document and in the source document. In some embodiments, the lengths of the documents are approximately equal if the length difference is no more than twenty-five percent (25%) of each of their lengths.

If the target document and the source document are approximately the same length, the document classification module 304 may, in exemplary embodiments, determine whether the number of sentence pairs identified between the target document and the source documents satisfies the noisy sentence threshold.

If the noisy sentence threshold is satisfied, the monotone sentence pairs between the target document and the source document are identified, in step 612. The monotone sentence pairs may be identified from the noisy sentence pairs previously identified. At step 614, whether the number of identified monotone sentence pairs satisfies the monotone sentence threshold determined in step 604 is determined. This determination may be performed by the document classification module 304, in accordance with exemplary embodiments.

If the monotone sentence threshold is satisfied, the source document and the target document are identified as parallel and added to the training set, in step 512. The steps performed in process 600 may be reordered or performed substantially simultaneously and still fall within the scope of exemplary embodiments. Additional determinations known by those skilled in the art may be added to process 600 and still be within the scope of various embodiments.

FIG. 7 depicts a flowchart of a process 700 for extracting parallel sentence fragments from comparable corpora, according to exemplary embodiments. In exemplary embodiments, the parallel fragment engine 210 may perform process 700. Parallel sentence fragments comprise sub-sentential parallel translations found within documents in bilingual collections. For example, corpora may be comparable if the source document and the target document are determined to not be parallel according to the process 600.

To extract parallel sentence fragments, a coarse lexicon and a fine lexicon are generated from an existing parallel training corpus in step 702. In exemplary embodiments, the coarse lexicon may comprise the coarse lexicon 404 and the fine lexicon may comprise the fine lexicon 410, discussed in FIG. 4. The coarse lexicon may be generated by running the GIZA++ implementation of the IBM word alignment models. The coarse lexicon is configured to identify most of the existing parallel data. The fine lexicon may be generated according to the process described in connection with FIG. 8 according to exemplary embodiments.

At step 704, sentence pairs which have few words that are translations of each other using the coarse lexicon are discarded. A lack of parallel words in a sentence pair according to the coarse lexicon indicates that it is unlikely that there are parallel fragments within the sentence pair.

At step 706, each word in the source sentence is greedily linked to the best translation in the target sentence. The word linkages are a way to quantify the differences between source sentences that are “mostly translated” and “mostly not translated” in the target sentences. By representing the target sentence as a numeric signal where translated words correspond to a positive value and not translated words correspond to a negative value, the process 700 is able to retain fragments within the sentence that are mostly positive. The word linkages may then be assigned a specific numerical value in steps 708 and 710.

At step 708, a positive value is assigned to the word linkages that indicate translated words in the sentence pair. The positive value corresponds to a positive dependency of the linked words. The numerical value is retrieved or otherwise accessed from the fine lexicon generated in step 702 based on the linked words.

At step 710, not translated words in the sentence pair are assigned a negative value to indicate that there is little or no probability that the words can, or should be, linked. In some embodiments, the value assigned to the not translated words is negative one (−1).

At step 712, a smoothing filter is applied to the numerical signal generated according to the sentence pair to obtain a filtered signal. In some embodiments, the smoothing filter may comprise an averaging filter which sets the value at each point to be the average of several values surrounding the value. For example, the surrounding five (5) values may be averaged.

At step 714, fragment matches within the sentence pair are determined. If there is a parallel fragment in the sentence pair, the positive fragments having a corresponding positive filtered signal are retained. If there is no fragment match, or the filtered signal is negative, the sentence pair is discarded.

At step 716, unlikely matching fragments are discarded. The application of the smoothing filter may, for example, distort the signal to produce a short fragment that is a false positive. For example, in some embodiments, fragments comprising less than three words may be discarded to avoid false positives. If the fragments that are likely to be a false positive are discarded, the process 700 returns to step 516 comprising adding the parallel fragments to the training set.

FIG. 8 depicts a flowchart of a process 800 for generating a fine lexicon, such as the fine lexicon 410, according to exemplary embodiments. The fine lexicon comprises fewer possible translations (e.g., an average of five (5) possible translations for each source word) for each source word than the coarse lexicon (e.g., coarse lexicon 404 which may comprise an average of twelve (12) possible translations). Thus, the fine lexicon is used to identify the fragment pairs that are likely to be parallel translations. For each possible translation, the fine lexicon comprises a positive numerical probability that the target word is a translation of the source word. Additionally, the fine lexicon also comprises a negative numerical probability that a target word is not a translation of a source word.

At step 802, a parallel training corpus is word aligned. Word aligning the parallel training corpus generates annotations in the parallel training corpus indicating that a word in the target language is a possible translation of a word in the source language.

At step 804, a Log-Likelihood-Ratio (LLR) score is calculated to measure the likelihood that a source word and a target word are not independent. The LLR score is used to estimate the independence of pairs of words which cooccur in a parallel corpus. According to exemplary embodiments, the source word and the target word will cooccur in the source document and the target document if the two words are also linked together in the word aligned parallel training corpus. According to some embodiments, the LLR score may be computed using the equation: ${\mathrm{LLR}}_{\mathrm{score}}=\sum _{t?\in \left\{t,-t\right\}}\sum _{s?\in \left\{s,-s\right\}}C\left(t?,s?\right)\log \left(\frac{p\left(t?|s?\right)}{p\left(t?\right)}\right)$ corresponding words that are linked together in the respective target and source language sentences of an aligned sentence pair. In this equation, −t and −s are the corresponding words that are not aligned in the respective sentences. The variables t? and S? range over these values, and C(t?, s?) is the observed joint count for the values of t? and s?. The probabilities in the formula refer to maximum likelihood estimates.

At step 806, both a positive probability and a negative probability are determined based on classifying the LLR score as indicating a positive association or a negative association. The positive probability estimates the probability that the target word translates into the source word. The negative probability estimates the probability that the target word does not translate into the source word.

At step 808, normalizing factors for each word in the source document are computed. One normalizing factor is based on the summation of the positive LLR scores while a second normalizing factor is based on a summation of the negative LLR scores.

At step 810, each term, or value associated with each word alignment, is divided by the corresponding normalizing factor calculated in step 808. In further embodiments of the fine lexicon, the probability distributions may be recalculated by reversing the source language and the target language. The reversed probability distributions may be calculated using process 800.

The above-described functions and components can be comprised of instructions that are stored on a storage medium. The instructions can be retrieved and executed by a processor. Some examples of instructions are software, program code, and firmware. Some examples of storage medium are memory devices, tape, disks, integrated circuits, and servers. The instructions are operational when executed by the processor to direct the processor to operate in accord with various embodiments. Those skilled in the art are familiar with instructions, processor(s), and storage medium.

While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. For example, any of the elements associated with the training set generator 108 may employ any of the desired functionality set forth hereinabove. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.

Claims

1. A method for identifying parallel sub-sentential fragments in a bilingual collection, the method comprising: translating a source document from a bilingual collection; querying a target library associated with the bilingual collection using the translated source document; identifying one or more target documents based on the query; aligning a source sentence associated with the source document to one or more target sentences associated with the one or more target documents; and determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.

2. The method recited in claim 1 further comprising discarding the one or more target sentences based on a coarse lexicon.

3. The method recited in claim 2 further comprising assigning a translation probability to words in the aligned source sentence based on a fine lexicon.

4. The method recited in claim 3 further comprising detecting a parallel fragment based on a threshold associated with a number of essentially continuous words assigned a positive translation probability.

5. The method recited in claim 1 further comprising determining whether the source document comprises a parallel translation of the target document.

6. A computer program embodied on a computer readable medium having instructions for identifying parallel sub-sentential fragments in a bilingual collection, the instructions comprising the steps: translating a source document from a bilingual collection; querying a target library associated with the bilingual collection using the translated source document; identifying one or more target documents based on the query; aligning a source sentence associated with the source document to one or more target sentences associated with the one or more target documents; and determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.

7. The computer program recited in claim 6 further comprising an instruction for discarding the one or more target sentences based on a coarse lexicon.

8. The computer program recited in claim 7 further comprising an instruction for assigning a translation probability to words in the aligned source sentence based on a fine lexicon.

9. The computer program recited in claim 8 further comprising an instruction for detecting a parallel fragment based on a threshold associated with a number of essentially continuous words assigned a positive translation probability.

10. The computer program recited in claim 6 further comprising an instruction for determining whether the source document comprises a parallel translation of the target document.

11. A method for identifying parallel documents in a bilingual collection, the method comprising: translating a source document from a bilingual collection; querying a target library associated with the bilingual collection using the translated source document; identifying one or more target documents based on the query; aligning a source sentence associated with the source document to one or more target sentences associated with the one or more target documents; and determining whether the source document comprises a parallel translation of one of the one or more target documents.

12. The method recited in claim 11 wherein determining whether the source document comprises a parallel translation of the one or more the target documents further comprises selecting one of the one or more target documents according to a number of the source sentences within the source document aligned to the one or more target sentences.

13. The method recited in claim 12 wherein determining whether the source document comprises a parallel translation of one of the one or more target documents further comprises determining whether the number of the source sentences aligned to the one or more target sentences satisfies a threshold associated with noisy sentences.

14. The method recited in claim 12 wherein determining whether the source document comprises a parallel translation of the target document further comprises determining whether the number of the source sentences aligned to the one or more target sentences satisfies a threshold associated with monotone sentences.

15. The method recited in claim 11 further comprising determining whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with one of the one or more target sentences.

16. A system for identifying parallel documents in a bilingual collection, the system comprising: a word translator engine configured to translate a source document from a bilingual collection; a query engine configured to query a target library associated with the bilingual collection using the translated source document and identify one or more target documents based on the query; a document selector engine configured to align a source sentence associated with the source document to one or more target sentences associated with the one or more target documents; and a parallel document engine configured to determine whether the source document comprises a parallel translation of one of the one or more the target documents.

17. The system recited in claim 16 wherein the parallel document engine further comprises a sentence analysis module configured to select the one of the one or more target documents according to a number of the source sentences within the source document aligned to the one or more target sentences.

18. The system recited in claim 17 wherein the parallel document engine further comprises a document classification module configured to determine whether the number of thee source sentences aligned to the one or more target sentences satisfies a threshold associated with noisy sentences.

19. The system recited in claim 17 wherein the parallel document engine further comprises a document classification module configured to determine whether the number of the source sentences aligned to the one or more target sentences satisfies a threshold associated with monotone sentences.

20. The system recited in claim 16 further comprising a parallel fragment engine configured to determine whether a source fragment associated with the source sentence comprises a parallel translation of a target fragment associated with the one or more target sentences.