Abstract:
Technology is disclosed for snippet pre-translation and dynamic selection of translation systems. Pre-translation uses snippet attributes such as characteristics of a snippet author, snippet topics, snippet context, expected snippet viewers, etc., to predict how many translation requests for the snippet are likely to be received. An appropriate translator can be dynamically selected to produce a translation of a snippet either as a result of the snippet being selected for pre-translation or from another trigger, such as a user requesting a translation of the snippet. Different translators can generate high quality translations after a period of time or other translators can generate lower quality translations earlier. Dynamic selection of translators involves dynamically selecting machine or human translation, e.g., based on a quality of translation that is desired. Translations can be improved over time by employing better machine or human translators, such as when a snippet is identified as being more popular.

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 14/981,769, filed on Dec. 28, 2015, and entitled “PREDICTING FUTURE TRANSLATIONS”; and is also a continuation of U.S. patent application Ser. No. 14/981,794, filed on Dec. 28, 2015, and entitled “PREDICTING FUTURE TRANSLATIONS”; the disclosures of which are hereby incorporated herein in their entireties by reference. 
     
    
     BACKGROUND 
       [0002]    The Internet has made it possible for people to connect and share information globally in ways previously undreamt of. Social media platforms, for example, enable people on opposite sides of the world to collaborate on ideas, discuss current events, or simply share what they had for lunch. The amount of content generated through such social media technologies and the load on these systems are staggering. It is common for social media providers to operate databases with petabytes of media items, while leading providers are already looking toward technology to handle exabytes of data. Furthermore, millions of users across the globe enjoy the ability to simultaneously interact with content on social media websites. One popular social media website, for example, has over a billion active users that spend a total of over ten million hours each month interacting with the website. These users can often produce hundreds of millions of content posts each day. When users access such websites, the social media website can select content such as other users&#39; posts, news feeds, event notifications, and advertisements to display to the users. 
         [0003]    Despite this complexity, the transition from one page to the next within a social networking website appears simple to end users, and unless this page retrieval process occurs with no perceptible delays, users may lose patience and simply navigate to a different website. In addition, providing content that users find relevant increases the chances that users will interact with those content items and that they will return to the website in the future. For example, being able provide quick and accurate translations of content originally written in a different source language increases users access to both content and other users, and thus their overall enjoyment of a social media system. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]      FIG. 1  is a block diagram illustrating an overview of devices on which some implementations of the disclosed technology can operate. 
           [0005]      FIG. 2  is a block diagram illustrating an overview of an environment in which some implementations of the disclosed technology can operate. 
           [0006]      FIG. 3  is a block diagram illustrating components which, in some implementations, can be used in a system employing the disclosed technology. 
           [0007]      FIG. 4  is a flow diagram illustrating a process used in some implementations for selecting snippets to pre-translate. 
           [0008]      FIG. 5  is a flow diagram illustrating a process used in some implementations for computing a pre-translation score for a selected snippet. 
           [0009]      FIG. 6  is a flow diagram illustrating a process used in some implementations for need-based selection of translation methods. 
       
    
    
       [0010]    The techniques introduced here may be better understood by referring to the following Detailed Description in conjunction with the accompanying drawings, in which like reference numerals indicate identical or functionally similar elements. 
       DETAILED DESCRIPTION 
       [0011]    Translation technology for selecting language snippets to pre-translate based on translation requirements and for selecting processes for performing translations based on when the translation will be needed are described herein. Selecting language snippets to pre-translate and selecting methods of translation can improve the speed and quality of translations while decreasing the cost. For example, in a social media website that performs millions of translations per day, the disclosed translation technology can improve the response time for translation requests by predicting that translation requests will occur and having translation content already prepared. Furthermore, translation requests that are predicted ahead of time can be performed at less costly off-peak times. In addition, a translation that is identified as not being needed immediately can be translated using slower but more accurate techniques. 
         [0012]    A “language snippet” or “snippet,” as used herein, is a digital representation including one or more words or character groups. A snippet can be a representation of a content item or language from a content item (e.g., one or more images, videos, or audio files), language from or associated with a content item, or any other context or metadata associated with the content item (e.g., object, location, or person identification; image, video, or audio characteristics; structured data provided by an originator of the content item; or any other information associated with the content item). While the description below refers to snippets when performing natural language processing on content items, other language formats can be used, such as audio or video language representations. 
         [0013]    Pre-translation of a snippet can include translating the snippet from a source language into one or more output languages prior to an actual request for the translation. Such pre-translations can occur, for example, at the time the snippet is created or when the snippet is identified as likely to be translated a threshold amount. Additional details regarding selecting snippets for pre-translation are provided below in relation to  FIG. 4 . When determining which snippets to pre-translate, a pre-translation system can assign a score to a snippet based on translation likelihood factors such as popularity, a likely audience, length, characteristics of an author of the snippet, etc. Additional details regarding computing pre-translation scores for a snippet are provided below in relation to  FIG. 5 . 
         [0014]    Once a snippet has been selected for translation, a translation system can examine expected time factors to select a method of translation that balances quality and timeliness. The translation system can examine time factors that indicate when, if, and how frequently translation requests for a snippet are likely to occur. The translation system can compare these factors to the processing requirements of various translation methods to select a method to use in translating the snippet. As these conditions change, such as when the end of a likely time period is approaching or a content item&#39;s popularity increases, this analysis can be recomputed to determine whether to re-translate the content item with a higher accuracy translation method. Additional details regarding selecting translation methods are provided below in relation to  FIG. 6 . 
         [0015]    Several implementations of the described technology are discussed below in more detail in reference to the figures. Turning now to the figures,  FIG. 1  is a block diagram illustrating an overview of devices  100  on which some implementations of the disclosed technology may operate. The devices can comprise hardware components of a device  100  that can determine whether a pre-translation of a snippet should be performed or how snippet translations are to be performed. Device  100  can include one or more input devices  120  that provide input to the CPU (processor)  110 , notifying it of actions. The actions are typically mediated by a hardware controller that interprets the signals received from the input device and communicates the information to the CPU  110  using a communication protocol. Input devices  120  include, for example, a mouse, a keyboard, a touchscreen, an infrared sensor, a touchpad, a wearable input device, a camera- or image-based input device, a microphone, or other user input devices. 
         [0016]    CPU  110  can be a single processing unit or multiple processing units in a device or distributed across multiple devices. CPU  110  can be coupled to other hardware devices, for example, with the use of a bus, such as a PCI bus or SCSI bus. The CPU  110  can communicate with a hardware controller for devices, such as for a display  130 . Display  130  can be used to display text and graphics. In some examples, display  130  provides graphical and textual visual feedback to a user. In some implementations, display  130  includes the input device as part of the display, such as when the input device is a touchscreen or is equipped with an eye direction monitoring system. In some implementations, the display is separate from the input device. Examples of display devices are: an LCD display screen, an LED display screen, a projected, holographic, or augmented reality display (such as a heads-up display device or a head-mounted device), and so on. Other I/O devices  140  can also be coupled to the processor, such as a network card, video card, audio card, USB, firewire or other external device, camera, printer, speakers, CD-ROM drive, DVD drive, disk drive, or Blu-Ray device. 
         [0017]    In some implementations, the device  100  also includes a communication device capable of communicating wirelessly or wire-based with a network node. The communication device can communicate with another device or a server through a network using, for example, TCP/IP protocols. Device  100  can utilize the communication device to distribute operations across multiple network devices. 
         [0018]    The CPU  110  can have access to a memory  150 . A memory includes one or more of various hardware devices for volatile and non-volatile storage, and can include both read-only and writable memory. For example, a memory can comprise random access memory (RAM), CPU registers, read-only memory (ROM), and writable non-volatile memory, such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, device buffers, and so forth. A memory is not a propagating signal divorced from underlying hardware; a memory is thus non-transitory. Memory  150  can include program memory  160  that stores programs and software, such as an operating system  162 , translation systems  164 , or other application programs  166 . Memory  150  can also include data memory  170  that can include snippets, threshold values, translation engines, score weighting factors, timing factors, configuration data, settings, user options or preferences, etc. which can be provided to the program memory  160  or any element of the device  100 . 
         [0019]    The disclosed technology can be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the technology include, but are not limited to, personal computers, server computers, handheld or laptop devices, cellular telephones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, or the like. 
         [0020]      FIG. 2  is a block diagram illustrating an overview of an environment  200  in which some implementations of the disclosed technology may operate. Environment  200  can include one or more client computing devices  205 A-D, examples of which can include device  100 . Client computing devices  205  can operate in a networked environment using logical connections  210  through network  230  to one or more remote computers such as a server computing device. 
         [0021]    In some implementations, server  210  can be an edge server which receives client requests and coordinates fulfillment of those requests through other servers, such as servers  220 A-C. Server computing devices  210  and  220  can comprise computing systems, such as device  100 . Though each server computing device  210  and  220  is displayed logically as a single server, server computing devices can each be a distributed computing environment encompassing multiple computing devices located at the same or at geographically disparate physical locations. In some implementations, each server  220  corresponds to a group of servers. 
         [0022]    Client computing devices  205  and server computing devices  210  and  220  can each act as a server or client to other server/client devices. Server  210  can connect to a database  215 . Servers  220 A-C can each connect to a corresponding database  225 A-C. As discussed above, each server  220  can correspond to a group of servers, and each of these servers can share a database or can have their own database. Databases  215  and  225  can warehouse (e.g. store) information such as snippets, threshold values, translation engines, score weighting factors, timing factors, configuration data, settings, user options or preferences, etc. Though databases  215  and  225  are displayed logically as single units, databases  215  and  225  can each be a distributed computing environment encompassing multiple computing devices, can be located within their corresponding server, or can be located at the same or at geographically disparate physical locations. 
         [0023]    Network  230  can be a local area network (LAN) or a wide area network (WAN), but can also be other wired or wireless networks. Network  230  may be the Internet or some other public or private network. Client computing devices  205  can be connected to network  230  through a network interface, such as by wired or wireless communication. While the connections between server  210  and servers  220  are shown as separate connections, these connections can be any kind of local, wide area, wired, or wireless network, including network  230  or a separate public or private network. 
         [0024]      FIG. 3  is a block diagram illustrating components  300  which, in some implementations, can be used in a system implementing the disclosed technology. The components  300  include hardware  302 , general software  320 , and specialized components  340 . As discussed above, a system implementing the disclosed technology can use various hardware including central processing units  304 , working memory  306 , storage memory  308 , and input and output devices  310 . Components  300  can be implemented in a client computing device such as client computing devices  205  or on a server computing device, such as server computing device  210  or  220 . 
         [0025]    General software  320  can include various applications including an operating system  322 , local programs  324 , and a BIOS  326 . Specialized components  340  can be subcomponents of a general software application  320 , such as local programs  324 . Specialized components  340  can include pre-translator  344 , pre-translation scorer  346 , translators  348 , translation timer  350 , translator selector  352 , and components which can be used for transferring data and controlling the specialized components, such as interface  342 . In some implementations, components  300  can be in a computing system that is distributed across multiple computing devices or can include an interface to a server-based application. 
         [0026]    Pre-translator  344  can be configured to receive snippets which may need to be pre-translated, such as through interface  342 . Pre-translator  344  can obtain a score for receive snippets, such as using pre-translation scorer  346 . In some implementations, multiple scores can be obtained for a snippet corresponding to multiple possible output languages or possible users. When one of these scores is above a threshold value, indicating the likelihood of a request for a translation of the snippet, or a number of requests for translations of the snippet, is sufficiently high, pre-translator  344  can obtain one or more translations of the snippet, such as by using translators  348 . The obtained pre-translation can be returned through interface  342 . 
         [0027]    Pre-translation scorer  346  can be configured to determine translation likelihood factors for a received snippet and compute one or more scores based on the translation likelihood factors. In various implementations, the translation likelihood factors can include one or more of: characteristics identified for an author of the snippet, one of more topics identified in the snippet, a likely audience of the snippet, users who have engaged with the snippet thus far, snippet length, snippet source language, snippet rating, a likely amount of time before a translation of the snippet is needed, or any combination thereof. 
         [0028]    In some implementations, characteristics identified for an author of the snippet can include one or more of: a language an author is identified as being facile with, an author&#39;s age, an author&#39;s gender, a location associated with an author, an occupation of an author, an education level of an author, a number of friends of an author, friends of an author who speak a language other than the language spoken by the author, etc. In some implementations, posts with specified topics can be selected for pre-translation based on those topics being identified as generally popular, popular within a particular geographic area or language speaking group, popular within a particular age range of users, etc. In some implementations, a likely audience of the snippet can be determined based on a historical audience for: snippets with the same or similar author, snippets with similar topics, snippets created for a similar virtual location, etc. In some implementations, factors relating to users who have engaged with the snippet thus far can include a total number of users who have viewed the snippet, a total number of translation requests for the snippet, an amount of time the snippet has been viewable, a number of interactions with the snippet such as “likes” or “follows,” particular users viewing or otherwise interacting with the snippet who have been identified as predictive of future trends or trend setters, etc. In some implementations, a user can “like” a content item including one or more snippets by actuating a control associated with the content item. 
         [0029]    In some implementations, a snippet rating can be based on where in a list of comments the snippet will, or is likely, to appear, which can be determined for particular users or for users generally. In some implementations, a likely amount of time before a translation is needed can be based on factors such as when users who are likely to request a translation of the snippet are likely to interact with a system that provides translations of the snippet. For example, a social media website can determine that a snippet is likely to be translated into German. That social media website can also determine that most users who speak German access that social media website during a particular timeframe when Germanic countries are awake. In this example, the timing translation factor can be low when a current time is relatively far from the time users in Germanic countries are likely to access the social media website, i.e., pre-translation is not immediately needed. Additional details regarding computing timing factors for a snippet are given below in relation to translation timer  350 . 
         [0030]    In some implementations, pre-translation scorer  346  can score a snippet multiple times as factors affecting the snippet&#39;s score(s) change. For example, as different topics are identified as popular or “trending,” or as the time draws closer to when translations of a snippet are expected to be needed, scores indicating whether a pre-translation of the snippet should be performed can be recomputed. 
         [0031]    Translators  348  can be configured to provide a translation of a snippet. Translators  348  can include various machine translation engines or interfaces for human translators to perform a translation of a snippet. In some implementations, translators  348  can be associated with quality values indicating reliability of a translator at producing a translation of a snippet from a source language to an output language. In some implementations, the quality of a translation produced by a translator is inversely proportional to an amount of time required for the translation to be performed. In some implementations, human translators provide the highest quality translations and multiple human translators can have different quality ratings or can have different quality ratings based on an area or topic of specialty of that human translator. Multiple machine translation engines can be included in translators  348  that perform different amounts of processing and different methods of processing on a snippet to produce different quality translations. 
         [0032]    In some implementations, translators  348  can be associated with timing values. In various implementations, these timing values can indicate an expected amount of time a corresponding translator will take to perform a translation of a snippet or a speed with which the corresponding translator is expected to perform translations. This timing value can indicate how long it can take to translate an average sized snippet with the corresponding translator or can be a value indicating how long the translator can take to translate a portion of a snippet, such as a single word or the average per-character translation time, which can be multiplied by the number of words or characters in a snippet. Timing values can be based on a record of translations performed by a particular translator, such as by benchmarking tests for a machine translation engine or typical translation times for a human translator. These values can also be set by a human, such as when a human translator provides translation time estimates or guarantees. 
         [0033]    Translation timer  350  can be configured to compute an amount of time before a translation of a snippet is needed. This amount can be an estimated amount of time before a first translation of the snippet is requested, an estimated amount of time before a peak number of translations are requested, or an established amount of time before a threshold amount or frequency of translations are requested. This amount of time value can be based on a determination of a likely audience of a snippet. This amount of time value can also be based on languages associated with the likely audience, geographical locations of the likely audience, typical login times identified for a particular audience, etc. 
         [0034]    Translator selector  352  can be configured to select one of translators  348  to perform a translation of a snippet received through interface  342 . In some implementations, translator selector  352  can accomplish this by determining whether a human or machine translation will be performed. This determination can be based on a comparison between an amount of time value provided by translation timer  350  and performance metrics associated with various of translators  348  to determine whether there is sufficient time available for a human translation of the snippet. In addition, in some implementations, this determination can be based on a score for the snippet determined by pre-translation scorer  346  indicating whether the need for translations of the snippet justifies the cost of performing human translation of the snippet. If translator selector  352  determines that the snippet should be sent to a human translator this can be accomplished using one of the translators of  348  as an interface to the human translator. The individual human translator can be selected by choosing a translator that, based on a corresponding timing value, can perform the translation before the time value provided by translation timer  350  expires. This choosing can also be based on specialties identified for particular human translators, such as by matching snippets with a particular topic or in a particular area to a translator specialized in that topic or area. 
         [0035]    If translator selector  352  determines that the snippet should be translated with a machine translator of translators  348 , a machine translator engine can be chosen based on the machine translator engine performance metrics (i.e., timing and quality scores). This choosing can include a comparison between an amount of time value provided by translation timer  350  and timing values associated with various machine translation engines. In some implementations, this choosing can be performed by balancing (A) a desired quality for the translation, corresponding to a likely amount of translations that will be required for the received snippet, represented by a score for the snippet determined by pre-translation scorer  346  with (B) the cost required for the translation, corresponding to an amount of processing required for a machine translation engine represented by a quality score for the machine translation engine. 
         [0036]    Those skilled in the art will appreciate that the components illustrated in  FIGS. 1-3  described above, and in each of the flow diagrams discussed below, may be altered in a variety of ways. For example, the order of the logic may be rearranged, substeps may be performed in parallel, illustrated logic may be omitted, other logic may be included, etc. 
         [0037]      FIG. 4  is a flow diagram illustrating a process  400  used in some implementations for selecting snippets to pre-translate. Process  400  begins at block  402  and continues to block  404 . At block  404 , process  400  can receive a snippet as a potential item for pre-translation. In some implementations, process  400  is performed for a social media website. In various implementations, the snippet can be from a user post to the social media website, a comment on another social media item, a news item, an event, etc. In some implementations where the snippet is from one of multiple comments on another social media item, process  400  can be performed for all comments or for only top ranked comments, which can be comments which are top ranked for all users or for individual users. 
         [0038]    At block  406 , process  400  can compute a score for the snippet received at block  404 . This score can indicate how likely the received snippet is to be translated at least once or translated a threshold amount. This score can be computed by determining translation likelihood factors for the snippet such as snippet author characteristics, user engagement, likely audience, etc.; computing weighting values for the determined translation likelihood factors; and computing a combined score for the snippet using the weighting values. In some implementations, multiple scores can be computed at block  406 . For example, process  400  can compute a score corresponding to different potential output languages for the translation of a snippet. Additional details regarding computing a snippet score are provided below in relation to  FIG. 5 . 
         [0039]    At block  408 , process  400  can compare the score computed at block  406  with a threshold. In some implementations where different scores are computed at block  406 , each score can be compared to the threshold or to a threshold corresponding to each output language. In some implementations, the loop between blocks  408 - 410  can be repeated for each score computed at block  406 . 
         [0040]    If at block  408 , the score is determined to be less than the threshold, process  400  determines that the snippet received at block  404  will not be pre-translated and continues to block  412 . If at block  408 , the score is determined to be more than the threshold, process  400  determines that the snippet received at block  404  will be pre-translated and continues to block  410 , wherein translations of the snippet can be obtained. Obtaining a translation of the snippet can include selecting a translation method for the snippet based on an estimation of when translations of the snippet will be needed and a balance between translation cost and quality. Based on the timing and quality/cost factors, a translation method can be selected from among one or more human translators or machine translation engines that can perform translations within different timeframes, at different quality levels, and at different costs. Additional details regarding selecting a translation method for a snippet are provided below in relation to  FIG. 6 . 
         [0041]    Process  400  then continues to block  412 , where it ends. Process  400  can be performed multiple times for the same snippet as pre-translation likelihood factors change. For example, a snippet can be re-classified to be pre-translated as translation factors change such as the number of users who view or interact with the snippet. 
         [0042]      FIG. 5  is a flow diagram illustrating a process  500  used in some implementations for computing a pre-translation score for a selected snippet. Process  500  begins at block  502  and continues to block  504 . At block  504 , process  500  can receive a snippet. In some implementations, process  500  can be performed as a sub-process of process  400  where the snippet received at block  504  is the snippet received at block  404 . In some implementations, process  500  can be performed independently of process  400 . 
         [0043]    At block  506 , process  500  can determine which of several translation likelihood factors apply to the snippet received at block  504 . Translation likelihood factors can be factors that indicate how many translations of the snippet will be requested or how urgent it is to perform those translations. In various implementations, translation likelihood factors can be selected based on: a predefined set of factors to use for computing a translation score, whether information relevant to a translation likelihood factor is available for the receipt snippet, an analysis of the snippet to determine which factors will provide the most accurate translation likelihood scores, etc. In various implementations, translation likelihood factors can include one or more of: characteristics identified for an author of the snippet, characteristics about the content of the snippet, a likely or expected audience of the snippet, user engagement with the snippet thus far, when the snippet was created, snippet source language, snippet rating, a likely amount of time before a translation is needed, a virtual location where the snippet is posted or created, or any combination thereof. 
         [0044]    In some implementations, characteristics identified for an author of the snippet can include one or more of: languages an author is identified as being facile with, an author&#39;s age, an author&#39;s gender, locations associated with an author, technology identified as being used by an author, content items the author has historically viewed or interacted with, occupation of an author, education level of an author, number of friends of an author, friends of an author who speak a language other than the language spoken by the author, etc. In some implementations, characteristics about the content of the snippet can include an identified topic or area of interest of the snippet, whether an identified topic or area of interest of the snippet is trending, snippet length, an language or dialect identified as the source the snippet was written in, types of words or slang used, etc. 
         [0045]    In some implementations, a likely audience of the snippet can be determined using historical viewership for snippets with similar topics or areas of interest, similar authors, similar virtual locations where the snippet originated, etc. In some implementations, characteristics of the likely audience can include audience gender, age, location, language, size, job, friends, education level, etc. In some implementations, characteristics of users who have engaged with the snippet thus far can include a total number of users, a total number of translation requests, an amount of time viewed, a number of interactions such as “likes” or “follows,” etc. In some implementations, a snippet can receive a rating, based on where in a list of comments the snippet will appear, which can be used to compute the snippet score. The snippet rating can be determined for particular users or for users generally. In some implementations, a likely amount of time before a translation is needed can be based on factors such as when users who are likely to request a translation of the snippet are likely to interact with a system that provides translations of the snippet. 
         [0046]    In some implementations, the translation likelihood factors determined at block  506  can be general across all users. In some implementations, the translation likelihood factors determined at block  506  can be specific to a particular user who is expected to request a translation or to a particular type of viewing user. For example, process  500  can be performed for a particular viewing user, and the likelihood factors can be determined in relation to whether it is likely that user will request a translation, such as whether the user is determined to have an interested in a topic identified in the snippet. 
         [0047]    In some implementations, process  500  can be performed multiple times for a single snippet for different potential output languages. The determined translation likelihood factors can be different for different output languages. For example, the likely audience in one output language can be different for one output language than for another. As a more specific example, a post to a virtual location associated with escargot can be determined to have a higher audience for a likely French speaking audience than an audience associated with German. 
         [0048]    At block  508 , process  500  can combine values associated with the translation likelihood factors determined at block  506  to compute a score for the received snippet. In some implementations, a value corresponding to a determined likelihood factor can be a set value for whether that factor applies to a snippet. For example, a binary translation likelihood factor, such as whether there have been at least 100 k views of the snippet, when true, can have a defined value such as 30. In some implementations, a value corresponding to a determined likelihood factor can be a value in a range corresponding to how strongly that factor applies to the received snippet. For example, a translation likelihood factor of friends of a snippet author who speak a language other than the language spoken by the author can be a value in the range between 1 and 100. This value could be, for example, the percentage of the author&#39;s friends who speak a language other than the language spoken by the author or a value originated as the total count of this type of author&#39;s friends which is normalized into a range. In some implementations, such a value can be computed as a formula. Continuing the previous example, the formula could be ⅓ times the number of friends of the author who speak a language other than the language spoken by the author. So if the number of this type of friend is 45, the value corresponding to this translation likelihood factor can be 15. 
         [0049]    In various implementations, the score for the received snippet can be: a sum of the values corresponding to the determined translation likelihood factors, an average of the values corresponding to the determined translation likelihood factors, a modification of a base value where the values corresponding to the determined likelihood translation factors are used as weighting values, or the result of another formula that places different emphasis on different of the values corresponding to the determined translation likelihood factors. At block  510 , process  500  can return the score or scores computed at block  508 . Process  500  then continues to block  512 , where it ends. 
         [0050]      FIG. 6  is a flow diagram illustrating a process  600  used in some implementations for need-based selection of translation methods. Process  600  begins at block  602  and continues to block  604 . At block  604 , process  600  can receive a snippet. In some implementations, process  600  can be performed as a sub-process of process  400 , in which case the snippet received at block  604  is the snippet received at block  404 . In some implementations, process  600  can be performed independently of process  400 . In some implementations, the received snippet can be associated with a score, such as the score computed at block  406 . 
         [0051]    At block  606 , process  600  can determine an output language or languages for the received snippet. In some implementations, the determined output language can be determined from a translation request. In some implementations, such as where the received snippet is being pre-translated, the output languages can be determined based on a likely audience or can be the output languages for which a score was determined to be above a threshold at block  408 . 
         [0052]    At block  608 , process  600  can compute an expected amount of time remaining before a translation is needed. In various implementations, this amount of time can be an amount of time before a first translation request is expected, an amount of time before a peak number of translation requests are received, or an amount of time before a threshold number of translation requests are received. The expected amount of time computed in block  608  can account for expected translation requests by a determined likely audience. For example, a determined likely audience can be associated with a particular location that typically makes translation requests through a social media system at a particular time of day, e.g., when the workday in that particular location ends, or typically does not use the social media system at other times of day, e.g., when users in this location are typically asleep. In some implementations, the amount of time remaining before a translation is needed can be based on a determined acceptable response time to a translation request. For example, a translation request can have been received 10 ms ago, and a determined acceptable response time for translation requests can be 25 ms; in this example, the computed time before a translation is needed would be 15 ms. 
         [0053]    At block  610 , process  600  can determine whether to use a machine translation engine to perform a translation of the received snippet. This determination can be based on a comparison between the time amount computed at block  608  and a speed or an expected amount of time required for a human translation of the received snippet, i.e. a “performance metric.” In various implementations, the speed or expected amount of time required for human translation can be based on factors such as: historically how much time has been required for human translations to be performed generally, estimates from human translators for how long it will take for translations to be performed, guarantees from human translators to deliver translations within a particular timeframe, a current queue for human translation requests, a current throughput for human translators, a number of currently available human translators, etc. In various implementations, these factors can be for snippets generally or based on snippet length. In various implementations, these factors can be determined for all available human translators or for a subset of human translators that are specialized in a particular topic or area corresponding to a determined topic or area for the received snippet. 
         [0054]    In some implementations, the determination of whether to perform a translation using a human translator or a machine translation engine can be based on a quality preference for the translation. In these implementations, various human translators and machine translation engines can be associated with a performance metric including a quality score indicating an expected quality provided by that method of translation. A quality preference for a translation can be based on factors such as an expected amount of translation requests for the snippet; a source of the snippet, i.e., snippets from some sources can be more critical to get correct than others; a topic of the snippet, i.e. a translation provider may have an interested in snippets with particular topics receiving enhanced translations; etc. 
         [0055]    Process  600  can perform the determination at block  610  for selecting a translation using a machine translation engine or a human translator by balancing an amount of time before an expected need for the translation expires and a preferred quality (or cost) of translation methods. For example, if a snippet is associated with a high score from block  406 , indicating, for example, that the snippet it will receive many translation requests or that the translation is part of a high-traffic webpage, this can indicate that it is worth paying a higher cost for a higher quality translation. This quality/cost can be balanced against a determination that the snippet will be needed very soon and thus should be performed by a lower quality but faster translation system. In some implementations, process  600  can determine that multiple types of translations should be applied for the same snippet. A higher quality translation system can be selected for a final translation of the snippet, but a faster, lower-quality transition system can be applied to respond to translation requests that arrive before the higher quality translation is complete. 
         [0056]    If process  600  determines at block  610  that a human translation should be obtained, process  600  continues to block  616 . At block  616 , process  600  can obtain a human translation of the received snippet. Obtaining a human translation can comprise sending the received snippet to a human translator. In some implementations, a human translator can be selected based on a specialty corresponding to the snippet, such as by topic, author, source language, virtual location. 
         [0057]    If process  600  determines at block  610  that a translation should be obtained using a machine translation engine, process  600  continues to block  612 . At block  612 , process  600  can select a machine translation engine for the received snippet. Similarly to block  610 , process  600  can further compare factors such as a number of expected translation requests, a preferred quality for the translation, an expected amount of time before the translation is needed, etc., to select a machine translation engine that has a performance metric indicating it is expected to provide a translation with sufficient quality within an acceptable amount of time. Also as discussed above in relation to block  610 , multiple machine translation engines can be selected to provide lower quality quick translations while waiting for higher-quality translations to be completed. In some implementations, multiple machine translation engines can be selected to generate translations in different output languages. In some implementations, block  610  can be combined with block  612  such that distinctions are not made between selecting a human translator or machine translation engine. 
         [0058]    At block  614 , process  600  can perform translations of the received snippet using the machine translation engines selected at block  612 . At block  618 , the translations attained at block  614  or at block  616  can be returned. Process  600  then proceeds to block  620  where it ends. Process  600  can be performed multiple times for the same snippet as quality or estimated timing factors change. For example, a snippet can be re-classified for a higher quality translation when a number of users who view or interact with the snippet increases or a topic associated with the snippet is determined to be trending. 
         [0059]    Several implementations of the disclosed technology are described above in reference to the figures. The computing devices on which the described technology may be implemented can include one or more central processing units, memory, input devices (e.g., keyboard and pointing devices), output devices (e.g., display devices), storage devices (e.g., disk drives), and network devices (e.g., network interfaces). The memory and storage devices are computer-readable storage media that can store instructions that implement at least portions of the described technology. In addition, the data structures and message structures can be stored or transmitted via a data transmission medium, such as a signal on a communications link. Various communications links can be used, such as the Internet, a local area network, a wide area network, or a point-to-point dial-up connection. Thus, computer-readable media can comprise computer-readable storage media (e.g., “non-transitory” media) and computer-readable transmission media. 
         [0060]    As used herein, being above a threshold means that a value for an item under comparison is above a specified other value, that an item under comparison is among a certain specified number of items with the largest value, or that an item under comparison has a value within a specified top percentage value. As used herein, being below a threshold means that a value for an item under comparison is below a specified other value, that an item under comparison is among a certain specified number of items with the smallest value, or that an item under comparison has a value within a specified bottom percentage value. As used herein, being within a threshold means that a value for an item under comparison is between two specified other values, that an item under comparison is among a middle specified number of items, or that an item under comparison has a value within a middle specified percentage range. 
         [0061]    As used herein, a condition being “expected” or “likely” means that a value has been computed for the condition and the value computed for the condition is above a threshold value. As used herein, a first element is “similar” to a second element by virtue of values being assigned to the first and second elements and a comparison between these values indicates the difference is below a threshold level. 
         [0062]    As used herein, the word “or” refers to any possible permutation of a set of items. For example, the phrase “A, B, or C” refers to at least one of A, B, C, or any combination thereof, such as any of: A; B; C; A and B; A and C; B and C; A, B, and C; or multiple of any item such as A and A; B, B, and C; A, A, B, C, and C; etc. 
         [0063]    Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Specific embodiments and implementations have been described herein for purposes of illustration, but various modifications can be made without deviating from the scope of the embodiments and implementations. The specific features and acts described above are disclosed as example forms of implementing the claims that follow. Accordingly, the embodiments and implementations are not limited except as by the appended claims. 
         [0064]    Any patents, patent applications, and other references noted above are incorporated herein by reference. Aspects can be modified, if necessary, to employ the systems, functions, and concepts of the various references described above to provide yet further implementations. If statements or subject matter in a document incorporated by reference conflicts with statements or subject matter of this application, then this application shall control.