CN103092827A - Method for multi-strategy interpreter manuscript automatic matching - Google Patents

Method for multi-strategy interpreter manuscript automatic matching Download PDF

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CN103092827A
CN103092827A CN2012105948711A CN201210594871A CN103092827A CN 103092827 A CN103092827 A CN 103092827A CN 2012105948711 A CN2012105948711 A CN 2012105948711A CN 201210594871 A CN201210594871 A CN 201210594871A CN 103092827 A CN103092827 A CN 103092827A
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translation
interpreter
contribution
manuscript
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CN103092827B (en
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江潮
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Transn Iol Technology Co ltd
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WUHAN TRANSN INFORMATION TECHNOLOGY Co Ltd
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Abstract

The invention provides a method for multi-strategy interpreter manuscript automatic matching. The method comprises the steps: reading each attribute of a to-be-translated manuscript, and screening out interpreters who meet the requirements of all attributes; reading identities (ID) of all interpreters which are screened out, carrying out similarity analysis between a post-translation manuscript of each interpreter and a standard manuscript, and determining a quantized value of translation capacity of each interpreter; determining a suitability value which is set for each interpreter; sequencing the sum of the quantized value and the suitability value of each interpreter, and selecting an interpreter who has a highest value. The method can objectively select the interpreters, efficiency is high, and through various attribute judgment, a plurality of most suitable interpreters are found out for user selection.

Description

The method of many tactful interpreter's contribution Auto-matchings
Technical field
The present invention relates to computer realm, in particular to a kind of method of how tactful interpreter's contribution Auto-matching.
Background technology
Information age and networking make the translation mode that very large variation occur.Utilize the translation flow management platform, according to different object stores talent's data.When translation duties is arranged, can be according to languages, article's style, professional domain and the client of the translation project requirement to translation quality and time limit, call most suitable translation and examine and revise personnel, form project team and translate, thus improve translation efficiency, save translation expense with, guarantee translation quality, optimization project management.
Present supplementary translation and the management platform coupling to interpreter and the manuscript of a translation, general or complete by artificial or half artificial mode, often need interpreter's (such as examining and revising) by higher level differentiate interpreter's translation ability and to the appropriate degree of the manuscript of a translation.So not only subjectivity is strong, and adopts artificial selection interpreter, inefficiency.
Summary of the invention
The present invention aims to provide a kind of method of how tactful interpreter's contribution Auto-matching, with the problem that solves.
In an embodiment of the present invention, provide a kind of method of how tactful interpreter's contribution Auto-matching, having comprised:
Read each attribute for the treatment of manuscript of a translation spare, filter out the interpreter who satisfies whole attribute specifications;
Read the described interpreter ID that filters out, the rear contribution of translating of each interpreter and the standard manuscript of a translation are carried out similarity analysis, determine the quantized value of each interpreter's translation ability;
Be defined as the appropriate degree value that each interpreter sets;
With each interpreter's quantized value, appropriate degree value and sort, select the highest interpreter of score value.
Embodiments of the invention can objectively be selected the interpreter, and efficient is higher, and through various determined property, find out optimal a plurality of interpreter and select for the user.
Description of drawings
Accompanying drawing described herein is used to provide a further understanding of the present invention, consists of the application's a part, and illustrative examples of the present invention and explanation thereof are used for explaining the present invention, do not consist of improper restriction of the present invention.In the accompanying drawings:
Fig. 1 shows the process flow diagram of embodiment;
Fig. 2 shows the module architectures that carries out similarity analysis in embodiment;
Fig. 3 shows the tree structure figure of keyword in embodiment.
Embodiment
Below with reference to the accompanying drawings and in conjunction with the embodiments, describe the present invention in detail.The process flow diagram of embodiment shown in Figure 1 comprises:
S11: read each attribute for the treatment of manuscript of a translation spare, filter out the interpreter who satisfies whole attribute specifications;
S12: read the described interpreter ID that filters out, the rear contribution of translating of each interpreter and the standard manuscript of a translation are carried out similarity analysis, determine the quantized value of each interpreter's translation ability;
S13: determine the appropriate degree value that each interpreter sets;
S14: with each interpreter's quantized value, appropriate degree value and sort, select the highest interpreter of score value.
Embodiments of the invention can objectively be selected the interpreter, and efficient is higher, and through various determined property, find out optimal a plurality of interpreter and select for the user.
Preferably, in embodiment, each interpreter is to should interpreter's gene attribute, the translation ability that has comprised languages, industry, subject, field etc., and credit rating, the timely degree of finishing the work, quality fluctuation situation, to the familiarity of certain contribution type, to other factors such as familiarity of certain class (individual) client; The gene attribute for the treatment of the contribution fragment of manuscript of a translation spare comprises languages, industry, subject, difficulty, translation brief etc.By with these gene attribute quantifications and be brought in a suitable Matching Model and mate, thereby draw the sequence which interpreter's translation certain contribution fragment is fit to and provides grade of fit.
Can find the interpreter according to three layers of Matching Model, ground floor is filtering item, and at first this layer determine some basic first terms, and the interpreter who does not meet these is filtered, and the interpreter who meets enters lower one deck coupling;
The second layer is the translation ability item, and this layer calculates the translation ability in the corresponding languages of interpreter, industry, subject, field by the gene attribute of the manuscript of a translation, and its value is the numerical value of 0～100;
The 3rd laminated appropriate computational item, system goes out according to interpreter's gene attribute and the weight calculation of attribute the appropriate degree that the interpreter translates this manuscript of a translation.
The matching process of ground floor comprises: read each attribute for the treatment of manuscript of a translation spare, filter out the interpreter who satisfies whole attribute specifications;
Determine this interpreter's state for after connecing the case state, read this interpreter and the described attribute for the treatment of that manuscript of a translation spare is identical; Whether attribute one of comprises at least: comprise the translation price, whether take over business, the rate of sending back the manuscript, translate requirements side, special contribution ability.
At least carry out one of following attribute selection:
If the value of described interpreter's translation price attribute is thought to meet attribute specification between the bound threshold values of the described translation price attribute for the treatment of manuscript of a translation spare;
If the value of described interpreter's the rate of sending back the manuscript attribute is thought to meet attribute specification less than the described value for the treatment of the rate of the sending back the manuscript attribute of manuscript of a translation spare;
If the content of described interpreter's translate requirements attribute comprises the described content for the treatment of the translate requirements attribute of manuscript of a translation spare, think to meet attribute specification;
If described interpreter's special contribution capabilities attribute meets the described content for the treatment of the special contribution attribute of manuscript of a translation spare, think to meet attribute specification.
For example: differentiate this interpreter and translate the translation price threshold values whether price surpasses the given manuscript of a translation, being no more than is 1, otherwise is that 0 this interpreter is filtered.
Whether take over business: wouldn't take over the business state if whether this interpreter of judgement is in, taking over business is 1, and not taking over business is that 0 this interpreter is filtered.
The rate of sending back the manuscript: if interpreter's rate of sending back the manuscript is not more than 50%, is included into the appropriate degree project by the size of its value and calculates, otherwise its value is 0 directly to filter out.
Translate requirements side whether: if the translation Party A has the translation brief of appointment, judge by its requirement whether the interpreter meets the requirements, meeting is 1, otherwise is that 0 this interpreter is filtered.
Special contribution ability: if treat that manuscript of a translation spare is the special contribution of certain class, as resume, notarization, legal documents or other, special form and type document are arranged, require the interpreter to possess relevant document translation ability, possessing is 1, otherwise is that 0 this interpreter is filtered.
After ground floor coupling, the gene attribute of the manuscript of a translation by the second layer calculates the translation ability in the corresponding languages of interpreter, industry, subject, field, and its value is the numerical value of 0～100.Quantize according to modular structure shown in Figure 2, the flowchart process of the method is as follows:
Extract the test contribution
The test contribution of making to the relevant motor car engine of English in one piece, its languages information, trade information and ambit information are:
Languages information: in → English
Trade information: 37 transportation equipment manufacturings
372 automobile makings
3721 vehicle complete vehicle manufacturings
Ambit information: 470 power and electrical engineering
470.30 Power Machinery Engineering
470.3020 internal-combustion engine engineering (comprising gasoline engine, diesel engine, gaseous propellant engine etc.)
Interpreter's ability is judged, if through judging, enter interpreter's ability selection/comparison module, if not through judging, carry out the translation ability test macro and log in module.
The interpreter logins the platform application and gets the test contribution.The attribute informations such as the languages of system's read test contribution, industry, subject, field if the interpreter possesses this attribute ability, enter interpreter's ability and compare/select module; Otherwise the translation test that the allocation for test license number carries out this specialty attribute ability for this interpreter;
The application to get interpreter carries out the translation ability test in these languages, industry, subject, field with test license number login translation ability test macro;
system is according to the test license number, extract 7 pieces of dependence test documents to interpreter to be measured from the standard testing document library, comprise 1 piece of languages class document, 3 piece (37 of industry class document, 372, 3721), 3 piece (470 of ambit class document, 470.30, 470.3020), its degree-of-difficulty factor is respectively: 9, 6, 8, 8, 7, 8, 7, put in order and be languages, industry-transportation equipment manufacturing (37), industry-transportation equipment manufacturing-automobile making (372), industry-transportation equipment manufacturing-automobile making-vehicle complete vehicle manufacturing (3721), ambit information-power and electrical engineering (470), ambit information-power and electrical engineering-Power Machinery Engineering (470.30), ambit information-power and electrical engineering-Power Machinery Engineering-internal-combustion engine engineering (470.3020), the numerical order of back is identical therewith,
After interpreter's translation is completed, the translation speed gear value that obtains 7 pieces of translations according to translate duration is respectively: 6,8,7,6,6,7,7, then call the similarity comparison module and calculate interpreter's translation and the similarity of standard translation, obtain the similarity value of 7 pieces of translations and standard translation, be respectively: 0.6,0.65,0.79,0.83,0.77,0.82,0.85;
Preferably, the process analyzed of contribution similarity comparison module comprises:
S21: extract every piece of whole keywords of translating the many pieces of standard manuscripts of a translation that belong to a specialty that rear contribution and the standard manuscript of a translation corresponding with it belong to, obtain keyword set C={k 1, k 2..., k m;
S22: calculate the probability that in C, each keyword k occurs in the contribution set, the contribution number of keyword k and the ratio of contribution sum namely occur, be designated as p(k).
Keyword is carried out descending sort by p (k), and with each keyword as a set, obtain so initial m set to be combined, be designated as { k 1), { k 2) ..., { k m);
In this m keyword, calculate at keyword k iIn the contribution that occurs, keyword k jThe probability that also occurs is designated as p (k j| k i), amount to Individual conditional probability, (1＜i, j≤m; I ≠ j);
p(k j| k i) computing method: p (k j| k i)=p (k jk i)/p (k i), p (k jk i) be k j, k iAppear at simultaneously the probability in same piece of writing contribution.
S23: merge set, merge when I and J satisfy following two conditions simultaneously when gathering:
Figure BDA00002688089500072
Satisfy p (k i) P1, p (k j| k i) P2;
Figure BDA00002688089500073
Satisfy | { k i∈ I ∪ J|p (k j| k i) P2}I (| I|+|J|)/2.(| X| represents to gather the number of element in x)
Finish when any two set merge when all meeting this two conditions, obtain simultaneously ground floor cluster keyword set C one C1, C2 ..., Cq).
To C one C1, C2 ..., Cq} gets threshold value P3＜P2, again carries out cluster with said method, generates the set of last layer concept.Repeat this process, until cluster set cluster again, these again the concept set of cluster be combined into the child node of root node C, so just generate the conceptional tree of keyword as shown in Figure 3.
S24: according to the tree structure that probability forms, calculate the similarity of two pieces of documents.
Phase Chi is with degree Sim ( A , B ) = A * B A * B A * B .
A={a 1, a 2..., a n, B={b 1, b 2..., b n, A, B are respectively the set of the keyword of correspondence in each contribution, a 1Be first keyword of A contribution, b 1First keyword for the B contribution.
In conceptional tree, the probability of each keyword is at the node location of conceptional tree, determines to translate the summation C of product of any two keywords of rear contribution and the standard manuscript of a translation; Wherein C = A * B = Σ i = 1 n Σ j = 1 n ( a i × b j ) ;
a i* b jBe the product of two keywords, the product of this keyword is: the path of the father node nearest with it according to any two leaf nodes, with the ratio of the degree of depth path of setting, as the product of these two leaf nodes; a i* b j=depth(com(a i, b j))/H, wherein, H is the pathdepth of tree.
After obtaining the value of similarity, extract every piece of keyword in the test contribution, mate with the term corpus, determine the term quantity of every piece of contribution, as the base value that calculates the contribution weight;
Go out every shared weight of test contribution by the translation degree-of-difficulty factor of described base value, described test contribution and interpreter's translation speed parameter identification;
The weighted value of above-mentioned gained is obtained numerical value between one 0～100 in conjunction with the similarity value of translating rear contribution and the standard manuscript of a translation, with this numerical value as this interpreter at this professional translation ability score value.
For example:
The weight calculation of 7 pieces of translations: the quantity of calculating the relevant speciality term of 6 pieces of industry classes and ambit class document according to the term corpus, the quantity that obtains the technical term of 6 pieces of documents is respectively 7,23,28,20,33,31, with the base value of these 6 numerical value as Determining Weights, because languages class document does not have technical term substantially, can get the arithmetic mean 23.7 of this 6 number as the weight base value of languages class, obtain like this calculating the base value set { 23.7 of 7 pieces of translation weights, 7,23,28,20,33,31};
The value correspondence of this weight set be multiply by the degree-of-difficulty factor { 9,6,8,8,7 of these 7 pieces of documents, 8,7} and translation speed gear value { 6,8,7,6,6,7,7} is gathered { 1279.7,336,1288,1344,840,1848,1519} does to this set the weighted value set { 0.15136,0.03974,0.15234 that normalized namely obtains these 7 pieces of articles, 0.15896,0.09935,0.21858,0.17966}.
Interpreter's technical translator ability score value calculates: with similarity value set { 0.6,0.65,0.79,0.83,0.77,0.82,0.85} and weight set { 0.15136,0.03974,0.15234,0.15896,0.09935,0.21858,0.17966} correspondence multiplies each other, and is gathered { 0.09082,0.02583,0.12035,0.13194,0.0765,0.17924,0.15271}, the value addition of this set be multiply by 100 more just obtains the interpreter the translation ability score value of these languages, industry, subject, domain attribute 77.739 minutes.
By the 3rd laminated appropriate computational item, system goes out according to interpreter's gene attribute and the weight calculation of attribute the value that the interpreter translates the appropriate degree of this manuscript of a translation.
The appropriate degree computational item comprises: the rate of sending back the manuscript, in time hand over original text rate, contribution available rate, translation cost performance, cooperation number of times, low staging error, whether signing.
Each numerical value between 0～100 wherein, circular is seen detailed description, wherein every shared weight such as following table:
Table 1
The appropriate degree computational item Weight
The rate of sending back the manuscript 0.15
In time hand over the original text rate 0.15
The contribution available rate 0.30
The translation cost performance 0.10
The cooperation number of times 0.15
Rudimentary error 0.15
Whether signing ?
The rate of sending back the manuscript: when the rate of sending back the manuscript is not more than 50%, enter the appropriate degree computational item, score value is calculated as it in (1-send back the manuscript rate) * 100.
In time hand over the original text rate: in time hand over original text rate * 100 to calculate score value as it.
The contribution available rate: score value is calculated as it in contribution available rate * 100.
The translation cost performance: the standard of this manuscript of a translation translation price/interpreter's translation price, become the numerical value between 0～100 to calculate score value as it according to the size conversion of its value, conversion table is as follows:
Table 2
Cost performance Be not more than 0.5 0.5～2 Be not less than 2
The gear score value 0 0.5 between～2, every 0.015, the gear value adds 1 100
The cooperation number of times: the successful cooperation number of times calculating gear score value table by interpreter and company is as follows:
Table 3
The cooperation number of times 0 time 1～3 time 4～10 times 11～20 Greater than 20
The gear score value 30 60 80 90 100
Low staging error: low staging error has reflected sense of responsibility and the translation attitude of interpreter's translation to a certain extent, sets corresponding gear score value table according to interpreter's rudimentary errors number as follows:
Table 4
Rudimentary errors number 0 time 1～5 time 5～10 times 11～20 20～40 Greater than 40 times
The gear score value 100 80 60 40 20 0
Whether signing: signing interpreter's appropriate degree score value adds 10 minutes.
The multiplied by weight that each appropriate degree computational item is corresponding with it, with the sum product of described a plurality of appropriate degree computational items as described and appropriate value.
At last, with each interpreter's quantized value, appropriate degree value and sort, select the highest interpreter of score value.
Describe in detail below by one piece of concrete waiting for translating original text.
The attribute information of this waiting for translating original text is as follows: the translation price is that standard translation price, translate requirements are without specific (special) requirements, non-special contribution.Its languages attribute arrives English in being, affiliated industry is automobile industry, and ambit information is machinery---engine.The attribute information matching degree of each interpreter's attribute information and the manuscript of a translation is as shown in table 5:
Table 5
Figure BDA00002688089500111
Figure BDA00002688089500121
Obtain shown in following table 6 after a filtration and calculating interpreter's translation ability score value after filtration:
Table 6
Figure BDA00002688089500122
Figure BDA00002688089500131
After calculating: the appropriate degree score of TR001, TR002, TR003, TR006 is respectively shown in following table 7: 84.3,87.3,85.8,76.8.
Table 7
Interpreter ID The appropriate degree score The translation ability score PTS
TR001 84.3+10 80 174.3
TR002 87.3+10 75 172.3
TR003 85.8+10 85 180.8
TR006 76.8 70 146.8
Select TR003 as the translation interpreter of the manuscript of a translation according to PTS, TR001 is the alternate translation interpreter of the manuscript of a translation.
pass through above-mentioned steps, find the interpreter of the most suitable translation contribution or contribution fragment, the present invention is because interpreter's gene attribute has comprised languages, industry, subject, the translation ability in field etc., and credit rating, finish the work and in time spend, the quality fluctuation situation, familiarity to certain contribution type, other factors such as familiarity to certain class (individual) client, the gene attribute of contribution fragment comprises languages, industry, subject, difficulty, translation brief etc., with these gene attribute quantifications and be brought in the gene Matching Model and mate, thereby draw the sequence certain which interpreter of contribution fragment is fit to translate and have grade of fit.The gene Matching Model is the comprehensive of a plurality of models such as translation ability coupling, appropriate degree coupling and similarity coupling, the foundation of this model solves the problems such as translation efficiency, quality and has very real effect for translation industry, for the foundation of the large-scale industrialized production of translation industry also significant.
obviously, those skilled in the art should be understood that, above-mentioned each module of the present invention or each step can realize with general calculation element, they can concentrate on single calculation element, perhaps be distributed on the network that a plurality of calculation elements form, alternatively, they can be realized with the executable program code of calculation element, thereby, they can be stored in memory storage and be carried out by calculation element, perhaps they are made into respectively each integrated circuit modules, perhaps a plurality of modules in them or step being made into the single integrated circuit module realizes.Like this, the present invention is not restricted to any specific hardware and software combination.
The above is only the preferred embodiments of the present invention, is not limited to the present invention, and for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (8)

1. the method for tactful interpreter's contribution Auto-matching more than a kind, is characterized in that, comprising:
Read each attribute for the treatment of manuscript of a translation spare, filter out the interpreter who satisfies whole attribute specifications;
Read the described interpreter ID that filters out, the rear contribution of translating of each interpreter and the standard manuscript of a translation are carried out similarity analysis, determine the quantized value of each interpreter's translation ability;
Be defined as the appropriate degree value that each interpreter sets;
With each interpreter's quantized value, appropriate degree value and sort, select the highest interpreter of score value.
2. method according to claim 1, is characterized in that, describedly treats that each attribute of manuscript of a translation spare one of comprises at least:
Translation price, the rate of sending back the manuscript, translate requirements, special contribution.
3. method according to claim 1, is characterized in that, the process of described screening comprises:
Determine this interpreter's state for after connecing the case state, read this interpreter and the described attribute for the treatment of that manuscript of a translation spare is identical;
At least carry out one of following attribute selection:
If the value of described interpreter's translation price attribute the described translation price attribute for the treatment of manuscript of a translation spare the bound threshold values between, think to meet attribute specification;
If the value of described interpreter's the rate of sending back the manuscript attribute is thought to meet attribute specification less than the described value for the treatment of the rate of the sending back the manuscript attribute of manuscript of a translation spare;
If the content of described interpreter's translate requirements attribute comprises the described content for the treatment of the translate requirements attribute of manuscript of a translation spare, think to meet attribute specification;
If the capabilities attribute of described interpreter's special contribution meets the described content for the treatment of the special contribution attribute of manuscript of a translation spare, think to meet attribute specification.
4. method according to claim 1, is characterized in that, it is one of following that the computational item of described appropriate degree comprises at least:
Send back the manuscript rate, in time hand over original text rate, contribution available rate, translation cost performance, cooperation number of times, rudimentary error; Wherein, numerical value between 0 ~ 100 of each correspondence.
The process of described definite appropriate degree value comprises:
Set the weight of a plurality of appropriate degree computational items;
The multiplied by weight that each appropriate degree computational item is corresponding with it, with the sum product of described a plurality of appropriate degree computational items as described and appropriate value.
5. method according to claim 1, is characterized in that, the process of described similarity analysis comprises:
Extract every piece of keyword of translating the standard manuscript of a translation set in rear contribution and corresponding with it test document storehouse; The set of this keyword is C={k 1, k 2..., k m;
Calculate the probability that in C, each keyword k occurs in contribution, the contribution number of keyword k and the ratio of contribution sum namely occur, be designated as p(k);
With keyword in C by p(k) carry out descending sort, and with each keyword as a set, obtain so initial m set to be combined, be designated as { k 1, { k 2..., { k m;
In this m keyword, calculate at keyword k iKeyword k in the contribution that occurs jThe probability that occurs is designated as p(k j| k i), amount to
Figure FDA00002688089400021
Individual conditional probability, (1≤i, j≤m; I ≠ j); P(k j| k i)=p(k jk i)/p(k i), p(k jk i) be k jAnd k iAppear at simultaneously the probability in same piece of writing contribution;
Merge set to be combined, generate the keyword conceptional tree take keyword set C as root node.
6. method according to claim 5, is characterized in that, described merging process comprises:
For two keyword set C1 to be combined and C2, the merging condition is: have k iBelong to C1, k jBelong to C2, and p(k i) threshold values P1, p(k j| k i) threshold values P2, work as p(k i) and p(k j| k i) during greater than described setting threshold values, keyword k iAnd k jExpress same concept, satisfy one of the merging condition of the set at its place;
Appoint to a keyword k in set after merging i, its with set in the keyword over half p(k that all satisfies condition j| k i) threshold values P2.
7. method according to claim 6, is characterized in that, definition H is the height of the conceptional tree that generates, definition depth(k) be the degree of depth of node k in tree, be from root node to limit number that this node experiences;
Definition com (k i, k j) be from node k iAnd k jNearest common father node;
The long-pending computing formula of any two keywords: k i* k j=depth(com(k i, k j))/H;
If vectorial A={a 1, a 2..., a n, B={b 1, b 2..., b n, the definition vector calculation:
Figure FDA00002688089400031
One in described A and B is the manuscript of a translation to be translated, and another is the corresponding standard manuscript of a translation;
Calculating formula of similarity is: Sim ( A , B ) = A * B A * B A * B .
8. method according to claim 7, is characterized in that, determines that the process of described quantized value comprises:
According to every piece of quantity of testing the technical term of contribution, determine the weight base value of this every piece test contribution; Wherein, described test contribution obtains contribution after described translating after through translation;
Go out every shared weight of test contribution by the translation degree-of-difficulty factor of described weight base value, described test contribution and interpreter's translation speed parameter identification;
The weighted value of above-mentioned gained is obtained numerical value between one 0～100 in conjunction with the similarity value of translating rear contribution and the standard manuscript of a translation, with this numerical value as described quantized value.
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CN106156008A (en) * 2015-04-07 2016-11-23 阿里巴巴集团控股有限公司 The interpretation method of information and device
CN105138521B (en) * 2015-08-27 2017-12-22 武汉传神信息技术有限公司 A kind of translation industry risk project general recommendations interpreter's method
CN105138521A (en) * 2015-08-27 2015-12-09 武汉传神信息技术有限公司 General translator recommendation method for risk project in translation industry
CN105224524A (en) * 2015-09-02 2016-01-06 网易有道信息技术（北京）有限公司 Document translation difficulty evaluation method and device
CN105224524B (en) * 2015-09-02 2022-01-25 网易有道信息技术（北京）有限公司 Document translation difficulty evaluation method and device
CN105279147A (en) * 2015-09-29 2016-01-27 武汉传神信息技术有限公司 Translator document quick matching method
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CN107885730A (en) * 2017-09-25 2018-04-06 沈阳航空航天大学 Translation knowledge method for distinguishing validity under more interpreter's patterns
CN108364214A (en) * 2018-02-08 2018-08-03 环宇爱译（北京）信息技术有限责任公司 Translation on line intelligence ordering system and method
CN108846590A (en) * 2018-07-10 2018-11-20 广州市联普翻译有限公司 A kind of translation on line order matching process, system, storage medium and terminal
CN108961009A (en) * 2018-07-10 2018-12-07 广州市联普翻译有限公司 A kind of settlement method, system, storage medium and terminal for translating order expense
CN109299737A (en) * 2018-09-19 2019-02-01 语联网（武汉）信息技术有限公司 Choosing method, device and the electronic equipment of interpreter's gene
CN109299737B (en) * 2018-09-19 2021-10-26 语联网（武汉）信息技术有限公司 Translator gene selection method and device and electronic equipment
CN109636199A (en) * 2018-12-14 2019-04-16 语联网（武汉）信息技术有限公司 A kind of method and system to match interpreter to manuscript of a translation part
CN110085256A (en) * 2019-03-21 2019-08-02 视联动力信息技术股份有限公司 Information processing method and device
CN112598231A (en) * 2020-12-11 2021-04-02 四川语言桥信息技术有限公司 Manuscript distribution method, device, equipment and storage medium
CN112766776A (en) * 2021-01-27 2021-05-07 语联网（武汉）信息技术有限公司 Translation order pushing method and system
WO2022160817A1 (en) * 2021-01-27 2022-08-04 语联网（武汉）信息技术有限公司 Translation order pushing method and system

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