Patent Publication Number: US-2022229548-A1

Title: Keyboard Automatic Language Identification and Reconfiguration

Description:
RELATED APPLICATION(S) 
     This application is a continuation of and claims priority to U.S. patent application Ser. No. 16/989,420, filed on Aug. 10, 2020, which in turn is a continuation of and claims priority to U.S. patent application Ser. No. 15/422,175, filed on Feb. 1, 2017, now U.S. Pat. No. 10,747,427, issued on Aug. 18, 2020, the disclosures of which are incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     Some graphical keyboards may rely on one or more models to determine what graphical keys a user may be selecting and/or what word or words the user may be typing, when providing input at the graphical keys. For example, a graphical keyboard may use a language model, a spatial model, and/or other model to perform tasks such as auto-correction, auto-completion, key selection, character, word or phrase prediction, and other keyboard input related tasks. Some models may be tailored to a particular language. In other words, some models may be configured to discern input assuming that a user is typing in a particular language. If a graphical keyboard tries to discern user input while the user is typing in a language that is different from what a model is expecting, the keyboard may exert abnormal or incorrect behavior resulting in errors in decoding input and/or a frustrating user experience. 
     SUMMARY 
     In general, this disclosure is directed to techniques for enabling a graphical keyboard to automatically determine one or more target languages associated with user input and, either automatically or in response to receiving a user input to a prompt requesting instructions to do change the active language decoder, reconfigure itself to enable language decoding in each of the one or more target languages. For example, a graphical keyboard of a graphical user interface (GUI) of a computing device may default to using an initial language decoder (e.g., a language model, a spatial model, and/or other type of model used to determine text from user input at a graphical keyboard) as a current decoder for decoding user inputs. The current language decoder may be configured to translate keyboard inputs into text of a default language, such as a language associated with a geographic location at which the computing device was sold and/or manufactured. A user of the computing device may provide inputs to the graphical keyboard to intentionally create text written in one or more target languages that differ from the language of the current decoder. 
     After receiving explicit consent to make use of and/or analyze information about the user of the computing device, and to enable more accurate input decoding, the graphical keyboard may use a language identification module (e.g., a machine-learned model) trained to determine whether any of the one or more target languages are unsupported by the current decoder, and if so, reconfigure itself to change decoders such that the graphical keyboard may decode inputs in the otherwise unsupported target language. In response to determining an unsupported target language, the graphical keyboard may generate a prompt (e.g., a graphical and/or audible alert) alerting the user to the option of reconfiguring the graphical keyboard to be able to decode inputs in the otherwise unsupported target languages. In other examples, the graphical keyboard may automatically reconfigure itself to without alerting the user. Once reconfigured, the graphical keyboard may automatically switch between multiple different decoders for subsequent user inputs so that the graphical keyboard uses the particular decoder associated with the target language determined by the language identification module. If the decoder associated with any of the one or more target languages is not currently installed as part of the graphical keyboard, the graphical keyboard may automatically download and/or install the different decoder (e.g., from a data repository at a remote server) without requiring the user to navigate through a menu of settings and options to download and install a new keyboard decoder and/or to toggle between keyboards of different languages. 
     By learning when and how to automatically reconfigure itself to be able to decode user inputs in multiple target languages, the graphical keyboard described herein may avoid incorrectly decoding user inputs and therefore reduce the number of user inputs required to perform text-entry. Exhibiting more accurate behavior and receiving fewer user inputs may simplify the user experience and may reduce power consumption of the computing device. 
     In one example, a method includes outputting, by a keyboard application executing at a computing device, for display, a graphical keyboard; determining, by the keyboard application, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, and determining, by a machine-learned model of the keyboard application, the target language of the text. The method further includes, if the target language of the text is not different than a language associated with the first decoder, outputting, by the keyboard application, for display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enabling, by the keyboard application, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and outputting, by the keyboard application, for display, an indication of one or more second candidate words determined by the second decoder from the text. 
     In another example, a mobile computing device includes at least one processor, and a memory. The memory stores instructions for a keyboard application that when executed cause the at least one processor to: output, for display at the presence-sensitive display, a graphical keyboard; determine, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, determine, using a machine-learned model, the target language of the text. The instructions, when executed, further cause the at least one processor to, if the target language of the text is not different than a language associated with the first decoder, output, for display at the presence-sensitive display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enable, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and output, for display at the presence-sensitive display, an indication of one or more second candidate words determined by the second decoder from the text. 
     In another example, a computer-readable storage medium encoded with instructions that, when executed by at least one processor of a computing device, cause the at least one processor to output, for display at the presence-sensitive display, a graphical keyboard; determine, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, determine, using a machine-learned model, the target language of the text. The instructions, when executed, further cause the at least one processor to, if the target language of the text is not different than a language associated with the first decoder, output, for display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enable, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and output, for display, an indication of one or more second candidate words determined by the second decoder from the text. 
     In another example, a system includes means for outputting, for display, a graphical keyboard; means for determining, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, and means for determining, by a machine-learned model, the target language of the text. The system further includes, if the target language of the text is not different than a language associated with the first decoder, means for outputting, for display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: means for enabling a second decoder, wherein a language associated with the second decoder matches the target language of the text; and means for outputting, for display, an indication of one or more second candidate words determined by the second decoder from the text. 
     The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIGS. 1A-1C  are conceptual diagrams illustrating a system including a computing device that executes an example graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. 
         FIG. 2  is a block diagram illustrating an example computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. 
         FIG. 3  is a block diagram illustrating an example computing device that outputs graphical content for display at a remote device, in accordance with one or more techniques of the present disclosure. 
         FIG. 4  is a flowchart illustrating example operations of a computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. 
         FIGS. 5-7  are conceptual diagrams illustrating example graphical user interfaces of an example computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1A-1C  are conceptual diagrams illustrating a system including a computing device that executes an example graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. System  100  includes information server system (“ISS”)  160  in communication with computing device  110  via network  130 . 
     Network  130  represents any public or private communications network, for instance, cellular, Wi-Fi, and/or other types of networks, for transmitting data between computing systems, servers, and computing devices. Network  130  may include one or more network hubs, network switches, network routers, or any other network equipment, that are operatively inter-coupled thereby providing for the exchange of information between ISS  160  and computing device  110 . Computing device  110  and ISS  160  may transmit and receive data across network  130  using any suitable communication techniques. 
     ISS  160  and computing device  110  may each be operatively coupled to network  130  using respective network links. The links coupling computing device  110  and ISS  160  to network  130  may be Ethernet, ATM or other types of network connections, and such connections may be wireless and/or wired connections. 
     ISS  160  represents any suitable remote computing system, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information both to and from a network, such as network  130 . ISS  160  hosts (or at least provides access to) a service for providing a computing device, such as computing device  110 , access information that is available (e.g., data) for download, install, and execution by the computing device. In some examples, ISS  160  represents a cloud computing system that is accessible via network  130 . For example, computing device  110  (e.g., UI module  120  and/or keyboard module  122 ) may communicate with ISS  160  via network  130  to access the prediction service provided by ISS  160 . In the example of  FIG. 1 , ISS  160  includes data repository module  162  and decoder package data store  132 . 
     Module  162  may perform operations described using software, hardware, firmware, or a mixture of hardware, software, and firmware residing in and/or executing at ISS  160 . ISS  160  may execute module  162  with multiple processors or multiple devices. ISS  160  may execute module  162  as a virtual machine executing on underlying hardware, as one or more services of an operating system or computing platform of ISS  160 , and/or as one or more executable programs at an application layer of a computing platform of ISS  160 . 
     Data repository module  162  may provide a digital distribution platform related to computing software, including software stored as one or more decoder packages at decoder package data store  132 . A decoder package may include one or more models (e.g., language model, spatial model, etc.), decoders, and/or other data necessary for a graphical keyboard to display a graphical keyboard layout and decode input detected at the keyboard layout into text of a particular written language. 
     Data repository module  162  may transmit data (e.g., one or more decoder packages) via network  130  in response to a request for data from a computing device, such as computing device  110 . Upon receipt, the computing device may install the data in memory and/or execute instructions of the data at a local processor of the computing device. For example, data repository module  126  may enable a user of computing device  110  to browse, search, select, purchase and/or cause computing device  110  to download and install one or more decoder packages stored at decoder package data store  132 . In some examples, repository module  126  may include information about the data stored at decoder package data store  132 , such as a description of each decoder package and/or user comments and reviews of each decoder package. Data repository module  126  may send information about one or more decoder packages to computing device  110  so that computing device  110  may display the information to a user of the computing device  110 . 
     Computing device  110  represents an individual mobile or non-mobile computing device. Examples of computing device  110  include a mobile phone, a tablet computer, a laptop computer, a desktop computer, a server, a mainframe, a set-top box, a television, a wearable device (e.g., a computerized watch, computerized eyewear, computerized gloves, etc.), or any other type of portable computing device, a personal digital assistants (PDA), portable gaming systems, media players, e-book readers, mobile television platforms, automobile navigation systems, automobile and/or home entertainment and infotainment systems, counter-top or mobile assistant devices (e.g., an “always listening” home assistant devices), or any other types of mobile, non-mobile, wearable, and non-wearable computing devices configured to receive information via a network, such as network  130 . 
     Computing device  110  includes presence-sensitive display (PSD)  112 , user interface (UI) module  120 , and keyboard module  122 . Modules  120 - 122  may perform operations described using software, hardware, firmware, or a mixture of hardware, software, and firmware residing in and/or executing at respective computing device  110 . Computing device  110  may execute modules  120 - 122  with multiple processors or multiple devices. Computing device  110  may execute modules  120 - 122  as virtual machines executing on underlying hardware. Modules  120 - 122  may execute as one or more services of an operating system or computing platform. Modules  120 - 122  may execute as one or more executable programs at an application layer of a computing platform. 
     PSD  112  of computing device  110  may function as an input and/or output device for computing device  110  and may be implemented using various technologies that enables computing device  110  to provide a user interface. PSD  112  may function as an input device using microphone technologies, infrared sensor technologies, presence-sensitive input screens, touchscreens (e.g., resistive touchscreens, surface acoustic wave touchscreens, capacitive touchscreens, projective capacitance touchscreens, acoustic pulse recognition touchscreens), pressure sensitive screens, or other input device technology for use in receiving user input. PSD  112  may function as an output (e.g., display) device using any one or more display devices (e.g., liquid crystal displays (LCD), dot matrix displays, light emitting diode (LED) displays, organic light-emitting diode (OLED) displays, e-ink, or similar monochrome or color displays capable of outputting visible information to a user of computing device  110 ), speaker technologies, haptic feedback technologies, or other output device technology for use in outputting information to a user. 
     PSD  112  may detect input (e.g., touch and non-touch input) from a user of respective computing device  110 . PSD  112  may detect indications of input by detecting one or more gestures from a user (e.g., the user touching, pointing, and/or swiping at or near one or more locations of PSD  112  with a finger or a stylus pen). PSD  112  may output information to a user in the form of a user interface (e.g., user interfaces  114 A- 114 C) which may be associated with functionality provided by computing device  110 . Such user interfaces may be associated with computing platforms, operating systems, applications, and/or services executing at or accessible from computing device  110  (e.g., electronic message applications, chat applications, Internet browser applications, mobile or desktop operating systems, social media applications, electronic games, and other types of applications). For example, PSD  112  may present user interfaces  114 A- 114 C (collectively referred to as “user interfaces  114 ”) which, as shown in  FIGS. 1A-1C , are graphical user interfaces of a chat application executing at computing device  110  and includes various graphical elements displayed at various locations of PSD  112 . 
     As shown in  FIGS. 1A-1C , user interfaces  114  are chat user interfaces, however user interfaces  114  may be any graphical user interface which includes a graphical keyboard. User interfaces  114  include output region  116 A, graphical keyboard  116 B, and edit region  116 C. A user of computing device  110  may provide input at graphical keyboard  116 B to produce textual characters within edit region  116 C that form the content of the electronic messages displayed within output region  116 A. The messages displayed within output region  116 A form a chat conversation between a user of computing device  110  and a user of a different computing device. 
     UI module  120  may manage user interactions with PSD  112  and other input and output components of computing device  110  as the interactions relate to the user interface(s) provided by computing devices  110 , including user interfaces  114 . In other words, UI module  120  may act as an intermediary between various components of computing device  110  to make determinations based on user input detected by PSD  112  and generate output at PSD  112  in response to the user input. For example, UI module  120  may receive instructions from an application, service, platform, or other module of computing device  110  to cause PSD  112  to output a user interface (e.g., user interfaces  114 ). UI module  120  may send commands and/or instructions to PSD  112  that cause PSD  112  to output user interface  114  for display. UI module  120  may manage inputs received by computing device  110  as a user views and interacts with the user interface presented at PSD  112  and update the user interface in response to receiving additional instructions from the application, service, platform, or other module of computing device  110  that is processing the user input. 
     Keyboard module  122  represents an application, service, or component executing at or accessible to computing device  110  that provides computing device  110  with a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language that has been determined, by the graphical keyboard, from the user inputs. Keyboard module  122  may perform traditional, graphical keyboard operations used for text-entry, such as: generating a graphical keyboard layout for display at PSD  112 , mapping detected inputs at PSD  112  to selections of graphical keys, determining characters based on selected keys, or predicting or autocorrecting words and/or phrases based on the characters determined from selected keys. Keyboard module  122  is responsible for controlling operations associated with graphical keyboard  116 B of user interfaces  114 . 
     In some examples, keyboard module  122  may be a stand-alone application, service, or module executing at computing device  110  and, in other examples, keyboard module  122  may be a single, integrated sub-component thereof For example, keyboard module  122  may be integrated into a chat or messaging application executing at computing device  110  whereas, in other examples, keyboard module  122  may be a stand-alone application or subroutine that is invoked by an application or operating platform of computing device  110  any time an application or operating platform requires graphical keyboard input functionality. If keyboard module  122  forms part of a chat or messaging application executing at computing device  110 , keyboard module  122  may provide the chat or messaging application with text-entry capability Similarly, if keyboard module  122  is a stand-alone application or subroutine that is invoked by an application or operating platform of computing device  110 , any time an application or operating platform requires graphical keyboard input functionality, keyboard module  122  may provide the invoking application or operating platform with text-entry. 
     In some examples, computing device  110  may download and install keyboard module  122  from a data distribution platform (e.g., via the Internet) such as data repository module  162  of ISS  160  or some other service provider. In other examples, keyboard module  122  may be preloaded during production of computing device  110  or be installed as part of installation of an encompassing software package (e.g., an operating system). 
     Graphical keyboard  116 B of user interfaces  114  includes graphical elements displayed as graphical keys  118 A and  118 B (collectively “graphical keys  118 ”) and word suggestion regions  119 A and  119 B (collectively “word suggestion regions  119 ”). Keyboard module  122  may output information to UI module  120  that specifies the layout of graphical keyboard  116 B within user interfaces  114 . For example, the information may include instructions that specify locations, sizes, colors, characters, text, and other characteristics of graphical keys  118  and word suggestion regions  119 . Based on the information received from keyboard module  122 , UI module  120  may cause PSD  112  display graphical keyboard  116 B as part of user interfaces  114 . 
     Each character key of graphical keys  118 A may be associated with a respective character (e.g., a letter, number, punctuation, or other character) displayed within the key or otherwise associated with the key. Each non-character key of graphical keys  118  may be associated with a function or command (e.g., emoji search, keyboard selector, etc.) of graphical keyboard  116 B. A user of computing device  110  may provide input at locations of PSD  112  at which one or more of graphical keys  118  are displayed to cause computing device  110  to input content (e.g., text) into edit region  116 C (e.g., for composing messages that are sent and displayed within output region  116 A). Keyboard module  122  may receive information from UI module  120  indicating locations associated with input detected by PSD  112  that are relative to the locations of each of graphical keys  118 . Using one or more decoders (e.g., a spatial model, language model, and/or other decoder component) keyboard module  122  may translate inputs at PSD  112  to selections of keys  118  and textual output (e.g., characters, words, and/or phrases of a language) at edit region  116 C. 
     For example, PSD  112  may detect user inputs as a user of computing device  110  provides the user inputs at or near a location of PSD  112  where PSD  112  presents graphical keys  118 . UI module  120  may receive, from PSD  112 , an indication of the user input detected by PSD  112  and output, to keyboard module  122 , information about the user input, such as an indication of one or more touch events (e.g., locations, pressure, and other information about the input). 
     Based on the information received from UI module  120 , one or more decoders of keyboard module  122  may map detected inputs at PSD  112  to selections of graphical keys  118 , determine characters based on selected keys  118 , and predict or autocorrect words and/or phrases determined based on the characters associated with the selected keys  118 . For example, a decoder of keyboard module  122  may include a spatial model that may determine, based on the locations of keys  118  and the information about the input, the most likely one or more keys  118  being selected. A language model of the decoder of keyboard module  122  may determine, based on the one or more keys  118  being selected, one or more characters, words, and/or phrases. In other words, a spatial model of a decoder of keyboard module  122  may determine a sequence of characters selected based on the one or more selected keys  118 , and a language model of a decoder of keyboard module  122  may determine one or more the most likely candidate letters, morphemes, words, and/or phrases that a user is trying to input based on the most likely keys  118  being selected. 
     Keyboard module  122  may send the sequence of characters and/or candidate words and phrases to UI module  120  and UI module  120  may cause PSD  112  to present the characters and/or candidate words determined from a selection of one or more keys  118  as text within edit region  116 C. In some examples, when functioning as a traditional keyboard for performing text-entry operations, and in response to receiving a user input at graphical keys  118  (e.g., as a user is typing at graphical keyboard  116 B to enter text within edit region  116 C), keyboard module  122  may cause UI module  120  to display the candidate words and/or phrases as one or more selectable spelling corrections and/or selectable word or phrase suggestions within a suggestion region  119  displayed adjacent to (e.g., above, below, or otherwise within graphical keyboard  116 B) graphical keys  118 . 
     While providing traditional keyboard functionality, keyboard module  122  may automatically determine one or more target languages associated with user input at graphical keyboard  116  and, either automatically or in response to prompting the user for instructions to do so, reconfigure itself to perform operations in the one or more target languages. For example, keyboard module  122  may default to using an initial language decoder (e.g., a language model, a spatial model, and/or other type of model used to determine text from user input at a graphical keyboard) as a current language decoder that is configured to translate keyboard inputs detected at PSD  112  into text of a default language (e.g., a language associated with a geographic location at which computing device  110  was sold and/or manufactured). Despite being configured to handle decoding in the default language, keyboard module  122  may receive user inputs detected by PSD  112  indicating that a user of computing device  110  is intentionally typing at graphical keyboard  116 B to create text written in one or more target languages that differ from the default language. 
     A language identification module configured as a model (e.g., a machine-learned model) executing in the background of keyboard module  122  may be trained to determine what one or more target languages that a user is typing in, and whether any of the one or more target languages are unsupported by its decoder. For example, the language identification module may be trained offline based on keyboard inputs from other users of other computing devices when those other users are typing in a different target language than the language of the keyboard decoder. For instance, the language identification module may be trained to determine what types of inputs a user makes at an English language graphical keyboard when typing words in a language other than English (e.g., Danish, Dutch, French, German, etc.). 
     If the language identification module determines that a user is providing inputs to graphical keyboard  116 B that differ from the language of the decoder(s) of keyboard module  122 , keyboard module  122  may automatically reconfigure itself to decode inputs in that target language. For example, if a decoder associated with any of the one or more target languages is not currently installed as part of keyboard module  122 , keyboard module  122  may automatically download and/or install the decoder needed to decode the target languages. Keyboard module  122  may communicate with data repository module  162  to obtain a decoder package for the target language(s) from data store  132 . Keyboard module  122  may receive the decoder package via network  130  and install the decoder package—all without requiring a user of computing device  110  to navigate through a menu of settings and options to download and install a new keyboard decoder. 
     Once reconfigured, keyboard module  122  may automatically switch between its multiple decoders for subsequent user inputs so that keyboard module  122  always uses the particular decoder that works with the target language determined by the language identification module. For example, as keyboard module  122  receives information from UI module  120  about user inputs detected by PSD  112  at graphical keyboard  116 B, the language identification module may initially determine a language associated with the user inputs. The language identification module may indicate to keyboard module  122  the target language of the input so that keyboard module  122  can automatically toggle to using the appropriate decoder for decoding the inputs. By learning when and how to automatically reconfigure itself to be able to decode user inputs in multiple target languages, keyboard module  122  may enable computing device  110  to avoid incorrectly decoding user inputs and therefore reduce the number of user inputs required to perform text-entry. Exhibiting more accurate behavior and receiving fewer user inputs may simplify the user experience of computing device  110  and may reduce power consumption of computing device  110 . 
     In operation, a user may rely on computing device  110  to exchange text messages by providing inputs to PSD  112  while PSD  112  displays user interfaces  114 . The user may be a native German speaker. Keyboard module  122  may be configured as an English based graphical keyboard application such that keyboard module  122  causes UI module  120  to display graphical keyboard  116 B having English language type graphical keys  118 A. 
     As shown in  FIG. 1A , computing device  110  may receive a message from a device associated with a friend that states, in German, “Wie geht&#39;s?” which translated to English, means “How are you?” Computing device  110  may output user interface  114 A for display which includes a message bubble with the message received from the device associated with the friend. 
     After viewing the message displayed at PSD  112 , the user of computing device  110  may provide input to select the English language graphical keys  118 A to compose a reply message, for instance, by gesturing at or near locations of PSD  112  at which keys  118 A are displayed. UI module  120  may send information to keyboard module  122  about the selection of keys  118 A. Keyboard module  122 , using an English language decoder, may determine text based on the information about the selection of keys  118 A. For example, keyboard module  122  may determine the text to be “es geht mir gut” which is meaningless in English, however, in German, translates to “I am doing well” in English. 
     Responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, a machine-learned model of keyboard module  122  may determine the target language of the text. For example, the language identification module of keyboard module  122  may require a sufficient amount of text (e.g., a minimum quantity of words or characters, a minimum byte length, etc.) before attempting to determine the target language of the text. By refraining from determining the target language unless the characteristic of the text satisfies the threshold, the language identification module may avoid wasting energy trying to compute the target language when doing so may not be very accurate. In the example of  FIG. 1A , the language identification module of keyboard module  122  may determine that the length of the text is sufficient for determining a target language associated with it and in response, determine the language of the text to be German. 
     If the target language of the text is not different than a language associated with the English decoder, keyboard module  122  may output, for display, an indication of one or more first candidate words determined by the English decoder from the text. For example, if the language identification module of keyboard module  122  identifies the language associated with the text to be English, keyboard module  1222  may send information to UI module  120  that causes PSD  112  to display, within word-suggestion region  119 A, one or more English language word suggestions that, keyboard module  122  has determined from the text. 
     If the target language of the text is different than the language associated with the English decoder keyboard module  122  may enable a German decoder and output, for display, an indication of one or more German candidate words determined by the German decoder from the text. In some examples, keyboard module  122  may automatically enable the German decoder and in other examples, keyboard module  122  may first prompt the user before enabling a different decoder. 
     For example, as shown in  FIG. 1B , if the language identification module of keyboard module  122  identifies the language associated with the text to be German, keyboard module  122  may cause UI module  120  to display at PSD  112  graphical indication  117  that includes information for alerting the user that keyboard module  122  has determined the user&#39;s inputs to be for typing German whereas keyboard module  122  is configured to translate keyboard inputs into English. Graphical indication  117  indicates that keyboard module  122  has automatically enabled a German decoder but also provides the user an opportunity to revert the reconfiguration by either clicking the undo button or going into the settings menu to manually adjust the keyboard settings. 
     As shown in  FIG. 1C , if the language identification module of keyboard module  122  identifies the language associated with the text to be German, keyboard module  1222  may send information to UI module  120  that causes PSD  112  to display, within word-suggestion region  119 B, one or more German language word suggestions that, keyboard module  122  has determined from the text. Also shown in  FIG. 1C , keyboard module  122 , in response to determining the target language that is different than the language of the English decoder, may cause UI module  120  and PSD  112  to output, for display, German language graphical keys  118 B that replace the English language graphical keys  118 A. For example, keyboard module  122  may send information to UI module  120  that causes PSD  112  to display, within word-suggestion region  119 B, one or more German language word suggestions that, keyboard module  122  has determined from the text. Keyboard module  122  may send further information to UI module  120  that causes PSD  112  change the layout of graphical keyboard  116  to be a German, as opposed to English, language graphical keyboard. 
     To enable the German decoder, keyboard module  122  may need to first download and install a decoder package associated with the target language. For example, keyboard module  122  may request, from data repository module  162 , a German decoder package. In response to the request, keyboard module  122  may receive data that once unpackaged, causes keyboard module  122  to install and enable the German decoder package including the German keyboard decoder defined by the data. 
       FIG. 2  is a block diagram illustrating an example computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. Computing device  210  of  FIG. 2  is described below as an example of computing device  110  of  FIGS. 1A-1C .  FIG. 2  illustrates only one particular example of computing device  210 , and many other examples of computing device  210  may be used in other instances and may include a subset of the components included in example computing device  210  or may include additional components not shown in  FIG. 2 . 
     As shown in the example of  FIG. 2 , computing device  210  includes PSD  212 , one or more processors  240 , one or more communication units  242 , one or more input components  244 , one or more output components  246 , and one or more storage components  248 . Presence-sensitive display  212  includes display component  202  and presence-sensitive input component  204 . Storage components  248  of computing device  210  include UI module  220 , keyboard module  222 , one or more application modules  224 , and one or more decoder package data stores  232 . Keyboard module  122  may include one or more decoder models  226 A- 226 N (collectively “decoder models  226 ”), installer model  228 , and language identification module  230 . Communication channels  250  may interconnect each of the components  212 ,  240 ,  242 ,  244 ,  246 ,  248 ,  220 ,  222 ,  224 ,  226 ,  228 , and  230  for inter-component communications (physically, communicatively, and/or operatively). In some examples, communication channels  250  may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data. 
     One or more communication units  242  of computing device  210  may communicate with external devices via one or more wired and/or wireless networks by transmitting and/or receiving network signals on the one or more networks. Examples of communication units  242  include a network interface card (e.g. such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and/or receive information. Other examples of communication units  242  may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers. 
     One or more input components  244  of computing device  210  may receive input. Examples of input are tactile, audio, and video input. Input components  242  of computing device  210 , in one example, includes a presence-sensitive input device (e.g., a touch sensitive screen, a PSD), mouse, keyboard, voice responsive system, video camera, microphone or any other type of device for detecting input from a human or machine. In some examples, input components  242  may include one or more sensor components one or more location sensors (GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more movement sensors (e.g., accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., microphone, camera, infrared proximity sensor, hygrometer, and the like). Other sensors may include a heart rate sensor, magnetometer, glucose sensor, hygrometer sensor, olfactory sensor, compass sensor, step counter sensor, to name a few other non-limiting examples. 
     One or more output components  246  of computing device  210  may generate output. Examples of output are tactile, audio, and video output. Output components  246  of computing device  210 , in one example, includes a PSD, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquid crystal display (LCD), or any other type of device for generating output to a human or machine. 
     PSD  212  of computing device  210  may be similar to PSD  112  of computing device  110  and includes display component  202  and presence-sensitive input component  204 . Display component  202  may be a screen at which information is displayed by PSD  212  and presence-sensitive input component  204  may detect an object at and/or near display component  202 . As one example range, presence-sensitive input component  204  may detect an object, such as a finger or stylus that is within two inches or less of display component  202 . Presence-sensitive input component  204  may determine a location (e.g., an [x, y] coordinate) of display component  202  at which the object was detected. In another example range, presence-sensitive input component  204  may detect an object six inches or less from display component  202  and other ranges are also possible. Presence-sensitive input component  204  may determine the location of display component  202  selected by a user&#39;s finger using capacitive, inductive, and/or optical recognition techniques. In some examples, presence-sensitive input component  204  also provides output to a user using tactile, audio, or video stimuli as described with respect to display component  202 . In the example of  FIG. 2 , PSD  212  may present a user interface (such as graphical user interfaces  114  of  FIGS. 1A-1C ). 
     While illustrated as an internal component of computing device  210 , PSD  212  may also represent an external component that shares a data path with computing device  210  for transmitting and/or receiving input and output. For instance, in one example, PSD  212  represents a built-in component of computing device  210  located within and physically connected to the external packaging of computing device  210  (e.g., a screen on a mobile phone). In another example, PSD  212  represents an external component of computing device  210  located outside and physically separated from the packaging or housing of computing device  210  (e.g., a monitor, a projector, etc. that shares a wired and/or wireless data path with computing device  210 ). 
     PSD  212  of computing device  210  may detect two-dimensional and/or three-dimensional gestures as input from a user of computing device  210 . For instance, a sensor of PSD  212  may detect a user&#39;s movement (e.g., moving a hand, an arm, a pen, a stylus, etc.) within a threshold distance of the sensor of PSD  212 . PSD  212  may determine a two or three dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a hand-wave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, PSD  212  can detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which PSD  212  outputs information for display. Instead, PSD  212  can detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which PSD  212  outputs information for display. 
     One or more processors  240  may implement functionality and/or execute instructions associated with computing device  210 . Examples of processors  240  include application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configure to function as a processor, a processing unit, or a processing device. Modules  220 ,  222 ,  224 ,  226 ,  228 , and  230  may be operable by processors  240  to perform various actions, operations, or functions of computing device  210 . For example, processors  240  of computing device  210  may retrieve and execute instructions stored by storage components  248  that cause processors  240  to perform the operations modules  220 ,  222 ,  224 ,  226 ,  228 , and  230 . The instructions, when executed by processors  240 , may cause computing device  210  to store information within storage components  248 . 
     One or more storage components  248  within computing device  210  may store information for processing during operation of computing device  210  (e.g., computing device  210  may store data accessed by modules  220 ,  222 ,  224 ,  226 ,  228 , and  230  during execution at computing device  210 ). For example, one or more storage components  248  may store decoder information at decoder package data store  232  that, when unpackaged and installed by installer module  228  of keyboard module  222 , enables keyboard module  222  to determine text, including candidate words in various languages, based on inputs at graphical keyboard  116 B. 
     In some examples, storage component  248  is a temporary memory, meaning that a primary purpose of storage component  248  is not long-term storage. Storage components  248  on computing device  210  may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. 
     Storage components  248 , in some examples, also include one or more computer-readable storage media. Storage components  248  in some examples include one or more non-transitory computer-readable storage mediums. Storage components  248  may be configured to store larger amounts of information than typically stored by volatile memory. Storage components  248  may further be configured for long-term storage of information as non-volatile memory space and retain information after power on/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components  248  may store program instructions and/or information (e.g., data) associated with modules  220 ,  222 ,  224 ,  226 ,  228 , and  230 . Storage components  248  may include a memory configured to store data or other information associated with modules  220 ,  222 ,  224 ,  226 ,  228 , and  230 . 
     UI module  220  may include all functionality of UI module  120  of computing device  110  of  FIGS. 1A-1C  and may perform similar operations as UI module  120  for managing a user interface (e.g., user interfaces  114 ) that computing device  210  provides at presence-sensitive display  212  for handling input from a user. For example, UI module  220  of computing device  210  may query keyboard module  222  for a keyboard layout. UI module  220  may transmit a request for a keyboard layout over communication channels  250  to keyboard module  222 . Keyboard module  222  may receive the request and reply to UI module  220  with data associated with the keyboard layout that keyboard module  222  determines is likely associated with a target language of a user. UI module  220  may receive the keyboard layout data over communication channels  250  and use the data to generate a user interface. UI module  220  may transmit a display command and data over communication channels  250  to cause PSD  212  to present the user interface at PSD  212 . 
     In some examples, UI module  220  may receive an indication of one or more user inputs detected at PSD  212  and may output information about the user inputs to keyboard module  222 . For example, PSD  212  may detect a user input and send data about the user input to UI module  220 . UI module  220  may generate one or more touch events based on the detected input. A touch event may include information that characterizes user input, such as a location component (e.g., [x,y] coordinates) of the user input, a time component (e.g., when the user input was received), a force component (e.g., an amount of pressure applied by the user input), or other data (e.g., speed, acceleration, direction, density, etc.) about the user input. 
     Based on location information of the touch events generated from the user input, UI module  220  may determine that the detected user input is associated the graphical keyboard. UI module  220  may send an indication of the one or more touch events to keyboard module  222  for further interpretation. Keyboard module  222  may determine, based on the touch events received from UI module  220 , that the detected user input represents a selection of one or more keys of the graphical keyboard. 
     Application modules  224  represent all the various individual applications and services executing at and accessible from computing device  210  that may rely on a graphical keyboard. A user of computing device  210  may interact with a graphical user interface associated with one or more application modules  224  to cause computing device  210  to perform a function. Numerous examples of application modules  224  may exist and include, a fitness application, a calendar application, a personal assistant or prediction engine, a search application, a map or navigation application, a transportation service application (e.g., a bus or train tracking application), a social media application, a game application, an e-mail application, a chat or messaging application, an Internet browser application, or any and all other applications that may execute at computing device  210 . 
     Keyboard module  222  may include all functionality of keyboard module  122  of computing device  110  of  FIGS. 1A-1C  and may perform similar operations as keyboard module  122  for providing a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs. Keyboard module  222  may include various submodules, such as one or more decoder modules  226 , installer module  228 , and language identification module  230 , which may perform the functionality of keyboard module  222 . 
     Decoder modules  226  determine text from inputs detected by PSD  112  at locations at which graphical keyboard  116 B is displayed. A single module  226  from decoder modules  226  may include a spatial model, a language model, or any other component or model used by keyboard module  222  to determine text based on keyboard inputs. For example, decoder module  226 A and decoder module  226 N may each include a respective spatial model, a respective language model, and a respective lexicon of one or more word from a dictionary. Each of decoders  226  may be associated with a language. Decoder  226 A may be associated with a particular language and decoder  226 N may be associated with a different language. 
     Decoder module  226 A may include a spatial model configured to receive one or more touch events as input, and output text as a character or sequence of characters that likely represents the one or more touch events, along with a degree of certainty or spatial model score indicative of how likely or with what accuracy the one or more characters define the touch events. In other words, the spatial model of decoder module  226 A may infer touch events as a selection of one or more keys of a keyboard and may output, based on the selection of the one or more keys, a character or sequence of characters. 
     Decoder module  226 A may include a language model configured to receive a character or sequence of characters as input, and output one or more candidate characters, words, or phrases that the language model identifies from a lexicon (e.g., a dictionary) as being potential replacements for a sequence of characters that the language model receives as input for a given language context (e.g., a sentence in a written language). Keyboard module  222  may cause UI module  220  to present one or more of the candidate words determined by a language model of decoder modes  226  at suggestion region  119 A or  119 B of user interfaces  114 A or  114 C. 
     Decoder module  226 A may include one or more lexicons (e.g., dictionaries) of words of a language that decoder module  226 A uses to perform traditional text-entry (e.g., auto-completion, auto-correction, spell check, word suggestion, etc.) operations. The lexicon may include a list of words within a written language vocabulary (e.g., a dictionary). For instance, the lexicon may include a database of words (e.g., words in a standard dictionary and/or words added to a dictionary by a user or computing device  210 ). A language model of decoder module  226 A may perform a lookup in the lexicon, of a character string, to determine one or more letters, words, and/or phrases that include parts or all of the characters of the character string. 
     Decoder package data stores  232  is similar to and includes all the functionality of decoder package data stores  123 . Decoder package data stores  232  includes decoder package  234 A- 234 N (collectively “decoder packages  234 ”). Each of decoder packages  234  is associated with a different language and includes instructions that, when installed as part of keyboard module  222  (e.g., as one of decoder modules  226 ), enables keyboard module  222  to produce a keyboard layout in the language and decode keyboard inputs into the language associated with that decoder package. Installer module  228  is configured to install decoder packages  234  that are downloaded and/or stored at data store  232 . Installer module  228  may unpackaged one of decoder packages  234  to produce a respective one of decoder modules  226 . 
     For example, decoder package  234 A may be associated with the Danish language. When decoder package  234 A is installed and enabled by installer module  228 , installer module  228  may generate decoder module  226 A. Decoder module  226 A of keyboard module  222  may cause PSD  212  to display a Danish keyboard layout for graphical keyboard  116 B and may interpret inputs detected at keyboard  116 B into text in the written Danish language. 
     Language identification module  230  is a machine-learned model (e.g., a long-short-term-memory-network or “LSTM network”) executing as part of keyboard module  222  for determining what one or more target languages that a user is typing in when providing inputs at graphical keyboard  116 , and whether any of the one or more target languages are unsupported by one of decoder modules  226 . Language identification module  230  may be divided into multiple levels of technology that act together to determine what one or more target languages that a user is typing in when providing inputs at graphical keyboard  116 . 
     A first level may be the core identification engine configured to return a probabilistic result (e.g., a probability or other numerical value indicative of a degree of likelihood) that classifies a piece of text into a particular language from a pre-defined set of languages known to module  230 . For example, language identification module  230  may determine, for each of a plurality of potential languages, a respective degree of likelihood or probability that the potential language is the target language of the text. Language identification module  230  may determine that the one or more potential languages from the plurality of potential languages with the highest respective degree of likelihoods are the target languages of the text. 
     The machine-learned model of language identification module  230  may be any supervised machine learning model. In some examples however, to achieve high accuracy, certain types of models may be better than others. For example, while a larger model, like a translation model executing at a server to provide on-demand translation service to the Internet, a smaller model that condenses the information of a large model may execute faster and with less memory, processing, and/or storage requirements. A smaller model that is trained to mimic the outputs of a larger model may execute faster and cheaper (e.g., from a computing resources perspective). For example, a recurrent neural network, such as a LSTM network trained with a cross-entropy criterion to predict the corresponding language may be used to determine a language from text. Rather than require a long input stream of text and compare the input stream to a plurality of different languages, the LSTM can use shorter portions of text input and execute faster by simply approximating the larger model&#39;s output. 
     The machine-learned model of language identification module  230  may be trained on various types of user information, such as which application a user is currently using, a user&#39;s typing history, or other kinds of information that may improve a target language determination. Context information may be used (e.g., location and time of computing device  210 , current activity being performed by the user of computing device  210 , sensor information obtained by computing device  210 , etc.) to train the machine-learned model of language identification module  230  to improve target language determinations. 
     Language identification module  230  may only make use of user information (e.g., content logs, user information, context information, etc.) about users of computing device  210  and/or users of other computing devices after receiving explicit permission to do so. In other words, language identification module  230  may be restricted from using information about a user to determine a target language, until computing device  210  obtains clear and unambiguous consent from the user to make use of and analyze information about the user and computing device  210 . For example, computing device  210  may cause PSD  212  to present a prompt asking the user to affirmatively give permission for computing device  210  to evaluate information about the user, computing device  210 , and information that computing device  210  receives that is to be presented to the user. The user may check a box in settings menu or affirmatively reply to the prompt to provide his or her consent. Computing device  210  may enable to the user to withdraw his or her consent at any time by unchecking the box or providing some other type of input to withdraw consent. 
     To reflect the real-world behavior of a large model, the model used by language identification module  230  may be trained based on data (e.g., content logs) received by keyboard applications executing at other computing devices. Said differently, the model of language identification module  230  may be trained based on user inputs (e.g., content logs) received by other keyboard applications executing at other computing devices. Language identification module  230  may be trained based on real-world data (e.g., content logs or other application data) obtained from keyboard applications executing on other computing devices to learn how users of the other computing devices provide inputs to their keyboards to type in a target language, without necessarily worrying about what the users are specifically typing (as far as content is concerned). In this way, language identification module  230 , by executing a smaller model, need not necessarily translate text or compare the text to all the words of a plurality of languages to determine the target language of the input. Using machine-learning on the input, the model of language identification module  230  may determine the language of the text using rules trained on previous inputs to infer what language is a user&#39;s target language. 
     A second level of language identification module  230  may be a layer of restrictions to ensure that performance is balanced for accuracy. That is, language identification module  230  may refrain from determining the target language of text inputs unless it determines a characteristic of the text satisfies a threshold for determining the target language of the text. The characteristic of the text may be a byte-length of the text, a minimum number of words associated with the text (e.g., as defined by a quantity of space delimiters in the text), and/or an average log probability associated with a frame of the text. Language identification module  230  may tune thresholds associated with one or more of these characteristics to achieve a balance of performance and accuracy. 
       FIG. 3  is a block diagram illustrating an example computing device that outputs graphical content for display at a remote device, in accordance with one or more techniques of the present disclosure. Graphical content, generally, may include any visual information that may be output for display, such as text, images, a group of moving images, to name only a few examples. The example shown in  FIG. 3  includes a computing device  310 , a PSD  312 , communication unit  342 , projector  380 , projector screen  382 , mobile device  386 , and visual display component  390 . In some examples, PSD  312  may be a presence-sensitive display as described in  FIGS. 1-2 . Although shown for purposes of example in  FIGS. 1 and 2  as a stand-alone computing device  110  and  210 , respectively, a computing device such as computing device  310  may, generally, be any component or system that includes a processor or other suitable computing environment for executing software instructions and, for example, need not include a presence-sensitive display. 
     As shown in the example of  FIG. 3 , computing device  310  may be a processor that includes functionality as described with respect to processors  240  in  FIG. 2 . In such examples, computing device  310  may be operatively coupled to PSD  312  by a communication channel  362 A, which may be a system bus or other suitable connection. Computing device  310  may also be operatively coupled to communication unit  342 , further described below, by a communication channel  362 B, which may also be a system bus or other suitable connection. Although shown separately as an example in  FIG. 3 , computing device  310  may be operatively coupled to PSD  312  and communication unit  342  by any number of one or more communication channels. 
     In other examples, such as illustrated previously by computing devices  110  and  210  in  FIGS. 1 and 2 , respectively, a computing device may refer to a portable or mobile device such as mobile phones (including smart phones), laptop computers, etc. In some examples, a computing device may be a desktop computer, tablet computer, smart television platform, camera, personal digital assistant (PDA), server, or mainframes. 
     PSD  312  may include display component  302  and presence-sensitive input component  304 . Display component  302  may, for example, receive data from computing device  310  and display the graphical content. In some examples, presence-sensitive input component  304  may determine one or more user inputs (e.g., continuous gestures, multi-touch gestures, single-touch gestures) at PSD  312  using capacitive, inductive, and/or optical recognition techniques and send indications of such user input to computing device  310  using communication channel  362 A. In some examples, presence-sensitive input component  304  may be physically positioned on top of display component  302  such that, when a user positions an input unit over a graphical element displayed by display component  302 , the location at which presence-sensitive input component  304  corresponds to the location of display component  302  at which the graphical element is displayed. 
     As shown in  FIG. 3 , computing device  310  may also include and/or be operatively coupled with communication unit  342 . Communication unit  342  may include functionality of communication unit  242  as described in  FIG. 2 . Examples of communication unit  342  may include a network interface card, an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other examples of such communication units may include Bluetooth, 3G, and WiFi radios, Universal Serial Bus (USB) interfaces, etc. Computing device  310  may also include and/or be operatively coupled with one or more other devices (e.g., input devices, output components, memory, storage devices) that are not shown in  FIG. 3  for purposes of brevity and illustration. 
       FIG. 3  also illustrates a projector  380  and projector screen  382 . Other such examples of projection devices may include electronic whiteboards, holographic display components, and any other suitable devices for displaying graphical content. Projector  380  and projector screen  382  may include one or more communication units that enable the respective devices to communicate with computing device  310 . In some examples, the one or more communication units may enable communication between projector  380  and projector screen  382 . Projector  380  may receive data from computing device  310  that includes graphical content. Projector  380 , in response to receiving the data, may project the graphical content onto projector screen  382 . In some examples, projector  380  may determine one or more user inputs (e.g., continuous gestures, multi-touch gestures, single-touch gestures) at projector screen using optical recognition or other suitable techniques and send indications of such user input using one or more communication units to computing device  310 . In such examples, projector screen  382  may be unnecessary, and projector  380  may project graphical content on any suitable medium and detect one or more user inputs using optical recognition or other such suitable techniques. 
     Projector screen  382 , in some examples, may include a presence-sensitive display  384 . Presence-sensitive display  384  may include a subset of functionality or all of the functionality of presence-sensitive display  112 ,  212 , and/or  312  as described in this disclosure. In some examples, presence-sensitive display  384  may include additional functionality. Projector screen  382  (e.g., an electronic whiteboard), may receive data from computing device  310  and display the graphical content. In some examples, presence-sensitive display  384  may determine one or more user inputs (e.g., continuous gestures, multi-touch gestures, single-touch gestures) at projector screen  382  using capacitive, inductive, and/or optical recognition techniques and send indications of such user input using one or more communication units to computing device  310 . 
       FIG. 3  also illustrates mobile device  386  and visual display component  390 . Mobile device  386  and visual display component  390  may each include computing and connectivity capabilities. Examples of mobile device  386  may include e-reader devices, convertible notebook devices, hybrid slate devices, etc. Examples of visual display component  390  may include other devices such as televisions, computer monitors, etc. In some examples, visual display component  390  may be a vehicle cockpit display or navigation display (e.g., in an automobile, aircraft, or some other vehicle). In some examples, visual display component  390  may be a home automation display or some other type of display that is separate from computing device  310 . 
     As shown in  FIG. 3 , mobile device  386  may include a presence-sensitive display  388 . Visual display component  390  may include a presence-sensitive display  392 . Presence-sensitive displays  388 ,  392  may include a subset of functionality or all of the functionality of presence-sensitive display  112 ,  212 , and/or  312  as described in this disclosure. In some examples, presence-sensitive displays  388 ,  392  may include additional functionality. In any case, presence-sensitive display  392 , for example, may receive data from computing device  310  and display the graphical content. In some examples, presence-sensitive display  392  may determine one or more user inputs (e.g., continuous gestures, multi-touch gestures, single-touch gestures) at projector screen using capacitive, inductive, and/or optical recognition techniques and send indications of such user input using one or more communication units to computing device  310 . 
     As described above, in some examples, computing device  310  may output graphical content for display at PSD  312  that is coupled to computing device  310  by a system bus or other suitable communication channel Computing device  310  may also output graphical content for display at one or more remote devices, such as projector  380 , projector screen  382 , mobile device  386 , and visual display component  390 . For instance, computing device  310  may execute one or more instructions to generate and/or modify graphical content in accordance with techniques of the present disclosure. Computing device  310  may output the data that includes the graphical content to a communication unit of computing device  310 , such as communication unit  342 . Communication unit  342  may send the data to one or more of the remote devices, such as projector  380 , projector screen  382 , mobile device  386 , and/or visual display component  390 . In this way, computing device  310  may output the graphical content for display at one or more of the remote devices. In some examples, one or more of the remote devices may output the graphical content at a presence-sensitive display that is included in and/or operatively coupled to the respective remote devices. 
     In some examples, computing device  310  may not output graphical content at PSD  312  that is operatively coupled to computing device  310 . In other examples, computing device  310  may output graphical content for display at both a PSD  312  that is coupled to computing device  310  by communication channel  362 A, and at one or more remote devices. In such examples, the graphical content may be displayed substantially contemporaneously at each respective device. For instance, some delay may be introduced by the communication latency to send the data that includes the graphical content to the remote device. In some examples, graphical content generated by computing device  310  and output for display at PSD  312  may be different than graphical content display output for display at one or more remote devices. 
     Computing device  310  may send and receive data using any suitable communication techniques. For example, computing device  310  may be operatively coupled to external network  374  using network link  373 A. Each of the remote devices illustrated in  FIG. 3  may be operatively coupled to network external network  374  by one of respective network links  373 B,  373 C, or  373 D. External network  374  may include network hubs, network switches, network routers, etc., that are operatively inter-coupled thereby providing for the exchange of information between computing device  310  and the remote devices illustrated in  FIG. 3 . In some examples, network links  373 A- 373 D may be Ethernet, ATM or other network connections. Such connections may be wireless and/or wired connections. 
     In some examples, computing device  310  may be operatively coupled to one or more of the remote devices included in  FIG. 3  using direct device communication  378 . Direct device communication  378  may include communications through which computing device  310  sends and receives data directly with a remote device, using wired or wireless communication. That is, in some examples of direct device communication  378 , data sent by computing device  310  may not be forwarded by one or more additional devices before being received at the remote device, and vice-versa. Examples of direct device communication  378  may include Bluetooth, Near-Field Communication, Universal Serial Bus, WiFi, infrared, etc. One or more of the remote devices illustrated in  FIG. 3  may be operatively coupled with computing device  310  by communication links  376 A- 376 D. In some examples, communication links  376 A- 376 D may be connections using Bluetooth, Near-Field Communication, Universal Serial Bus, infrared, etc. Such connections may be wireless and/or wired connections. 
     In accordance with techniques of the disclosure, computing device  310  may be operatively coupled to visual display component  390  using external network  374 . Computing device  310  may output, for display at PSD  312 , a graphical user interface including an edit region and a graphical keyboard the graphical keyboard including a plurality of For instance, computing device  310  may send data that includes a representation of the graphical user interface to communication unit  342 . Communication unit  342  may send the data that includes the representation of the graphical user interface to visual display component  390  using external network  374 . Visual display component  390 , in response to receiving the data using external network  374 , may cause PSD  312  to output the graphical user interface. In response to receiving a user input at PSD  312  to select one or more keys of the keyboard of the graphical user interface, visual display device  130  may send an indication of the selection of the one or more keys to computing device  310  using external network  374 . Communication unit  342  of may receive the indication of the selection of the one or more keys, and send the indication of the selection of the one or more keys to computing device  310 . 
     While receiving the indication of the selection of the one or more keys, computing device  310  may determine, using a first decoder of a keyboard application executing at computing device  310  and based on a selection of one or more keys of the graphical keyboard, text. Responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, a machine-learned model of the keyboard application executing at computing device  310  may determine the target language of the text. 
     If the target language of the text is not different than a language associated with the first decoder, computing device  310  may output, for display, an indication of one or more first candidate words determined by the first decoder from the text. For example, computing device  310  may send an updated representation of the graphical user interface that includes the one or more candidate words written in the language of the first decoder, within a suggestion region of the graphical keyboard. Communication unit  342  may receive the representation of the updated graphical user interface and may send the updated representation to visual display component  390 , such that visual display component  390  may cause PSD  312  to output the updated graphical user interface, including the candidate words displayed within the suggestion region of the graphical keyboard. 
     If the target language of the text is different than the language associated with the first decoder: computing device  310  may enable a second decoder of the keyboard application wherein has a language associated with the second decoder matches the target language of the text, and may output, for display, an indication of one or more second candidate words determined by the second decoder from the text. For example, computing device  310  may send an updated representation of the graphical user interface that includes the one or more candidate words written in the language of the second decoder within the suggestion region of the graphical keyboard. Computing device  310  may also send an updated layout of the graphical keys that is associated with the language of the second decoder. Communication unit  342  may receive the representation of the updated graphical user interface and may send the updated representation to visual display component  390 , such that visual display component  390  may cause PSD  312  to output the updated graphical user interface, including the candidate words displayed within the suggestion region of the graphical keyboard and the updated layout of the graphical keys. 
       FIG. 4  is a flowchart illustrating example operations of a computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure. The operations of  FIG. 4  may be performed by one or more processors of a computing device, such as computing devices  110  of  FIG. 1  or computing device  210  of  FIG. 2 . For purposes of illustration only,  FIG. 4  is described below within the context of computing devices  110  of  FIGS. 1A-1C . 
     In operation, computing device  110  may output, for display, a graphical keyboard ( 400 ). For example, keyboard module  122  may send instructions to UI module  120  that causes PSD  112  to present user interface  114 A. 
     Computing device  110  may determine, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text ( 410 ). For example, using an English language decoder, keyboard module  122  may process input information received from UI module  120  about touch inputs detected at PSD  112  at or near locations of PSD  112  at which graphical keys  118 A are displayed. 
     Computing device  110  may determine whether a characteristic of the text satisfies a threshold for determining a target language of the text ( 420 ). For example, keyboard module  122  may determine whether the user has provided sufficient text input to make verifying the language configuration of keyboard module  122  a worthwhile and not overly consuming process that could detract from usability or efficiency of the system. Keyboard module  122  may determine, after detecting a sufficient quantity of text (e.g., minimum quantity of words and/or byte-length), to verify whether the decoder being used matches the target language of the input ( 420 , YES branch). Otherwise, computing device  110  will continue to determine text using the first decoder ( 420 , NO branch). 
     Responsive to determining that the characteristic of the text satisfies the threshold for determining the target language of the text, computing device  110  may determine, using a machine-learned model, the target language of the text ( 430 ). For example, a LSTM network of keyboard module  122  may determine the target language of the text using rules developed from content log data obtained and analyzed from keyboard applications executing at other devices. The LSTM network may approximate the output of a large-scale machine-learning system executing at a remote computing device without necessarily performing the same analysis on the text input. 
     Computing device  110  may determine whether the target language is different than the first decoder ( 440 ). If the target language of the text is not different than a language associated with the first decoder ( 440 , NO branch), computing device  110  may output an indication of one or more first candidate words determined by the first decoder from the text. In other words, if the determined language corresponds to the language of the decoder used by keyboard module  122 , keyboard module  122  may cause UI module  120  to display candidate words determined by the first decoder at PSD  112 . 
     If the target language of the text is different than the language associated with the first decoder ( 440 , YES branch), computing device  110  may enable, a second decoder, wherein a language associated with the second decoder matches the target language of the text, and output an indication of one or more second candidate words determined by the second decoder from the text. For example, keyboard module  122  may enable a different decoder that can handle processing input in the target language. In some cases, keyboard module  122  may output a graphical indication to allow the user to approve or deny the enablement of the second decoder. 
     In some examples, computing device  110  may enable the second decoder comprises by downloading, by the keyboard application, from a remote computing system, a decoder package that includes instructions for executing the second decoder, installing, by the keyboard application, the decoder package. For example, keyboard module  122  may request and obtain a decoder package from ISS  160  and in response to obtaining the decoder package, unpack and install the decoder defined by the package so that subsequent text can be decoded using the decoder from the newly obtained and installed decoder package. 
       FIGS. 5-7  are conceptual diagrams illustrating example graphical user interfaces of an example computing device that includes a graphical keyboard configured to automatically reconfigure itself to decode user inputs into text of a target language determined based on the user inputs, in accordance with one or more aspects of the present disclosure.  FIGS. 5-7  illustrate, respectively, example user interfaces  514 ,  614 , and  714 . Each of user interfaces  514 ,  614 , and  714  may correspond to a graphical user interface displayed by computing devices  110 ,  210 , or  310  of  FIGS. 1, 2, and 3  respectively. 
       FIG. 5  shows an example user interface  514  that represents a page of a settings menu associated with computing device  110  and/or keyboard module  122 . As shown in settings option  590 , a user may be provided with an opportunity to control whether keyboard module  122  analyzes the keyboard inputs detected at PSD  112  for a language. By selecting option  590 , the user may enable target language detection and by unselecting option  590 , the user may disable target language detection. 
       FIG. 6  shows an example user interface  614  that includes output region  616 A, edit region  616 C, and graphical keyboard  616 B. User interface  614  shows how, after determining that the target language of the text inferred from inputs at a first graphical keyboard, keyboard module  122  may cause UI module  120  and UID  112  to display a second graphical keyboard associated with the target language that replaces the first graphical keyboard. In other words, if the graphical keyboard initially shown by computing device  110  was an English language keyboard (e.g.,  116 B), keyboard module  122  may cause computing device  110  to display a German language keyboard  616 B that replaces the English language keyboard. As shown in the example of  FIG. 6 , spacebar key  690  of graphical keys  618  includes an indication the target language and the language of the first decoder to indicate that one or more decoders are enabled. 
       FIG. 7  shows user interface  714  that includes output region  716 A, edit region  716 C, and graphical keyboard  716 B. User interface  714  shows how after determining that the target language of the text inferred from inputs differs from the decoder, and then after enabling a second decoder to correspond to the target language, computing device  110  may determine at least one other target language of text inferred from user inputs and may enable the at least one third decoder in response. In other words, keyboard module  122  may periodically determine whether the target language of user inputs corresponds to one of the enabled decoders and if not, keyboard module  122  may enable a decoder to handle it. As shown in the example of  FIG. 7 , spacebar key  790  of graphical keys  718  includes an indication of three decoders (EN, DE, and FR) being enabled simultaneously in response to determining that the user of computing device  110  is multilingual and typing in each of English, German, and French, at user interface  714 . 
     The following numbered clauses may illustrate one or more aspects of the disclosure: 
     Clause 1. A method comprising: outputting, by a keyboard application executing at a computing device, for display, a graphical keyboard; determining, by the keyboard application, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, determining, by a machine-learned model of the keyboard application, the target language of the text; determining whether the target language is different than a language associated with the first decoder; if the target language of the text is not different than a language associated with the first decoder, outputting, by the keyboard application, for display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enabling, by the keyboard application, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and outputting, by the keyboard application, for display, an indication of one or more second candidate words determined by the second decoder from the text. 
     Clause 2. The method of clause 1, further comprising: training, by the keyboard application, the machine-learned model based on user inputs received by other keyboard applications executing at other computing devices, wherein the second decoder was enabled by at least some of the other keyboard applications while receiving the user inputs. 
     Clause 3. The method of any one of clauses 1 or 2, wherein the characteristic of the text is a byte-length of the text. 
     Clause 4. The method of any one of clauses 1-3, wherein the characteristic of the text is a minimum number of words associated with the text. 
     Clause 5. The method of any one of clauses 1-4, wherein the characteristic of the text is an average log probability associated with the text. 
     Clause 6. The method of any one of clauses 1-5, wherein determining the target language of the text comprises: determining, by the machine-learned model, for each of a plurality of potential languages, a respective degree of likelihood that the potential language is the target language of the text; and determining, by the machine-learned model, that the potential language from the plurality of potential languages with the highest respective degree of likelihood is the target language of the text. 
     Clause 7. The method of any one of clauses 1-6, wherein the machine-learned model is a long short-term memory network. 
     Clause 8. The method of any one of clauses 1-7, wherein enabling the second decoder comprises: downloading, by the keyboard application, from a remote computing system, a decoder package that includes instructions for executing the second decoder; and installing, by the keyboard application, the decoder package. 
     Clause 9. The method of any one of clauses 1-8, wherein the graphical keyboard comprises a first graphical keyboard associated with the language of the first decoder, the method further comprising: outputting, by the keyboard application, for display, a second graphical keyboard associated with the target language that replaces the first graphical keyboard. 
     Clause 10. The method of clause 9, wherein a respective character of at least one key of the second graphical keyboard is different than a respective character of a corresponding key of the first graphical keyboard. 
     Clause 11. The method of any one of clauses 9 or 10, wherein a spacebar key of the second graphical keyboard includes an indication the target language. 
     Clause 12. The method of clause 11, wherein the spacebar key of the second graphical keyboard further includes an indication the language of the first decoder. 
     Clause 13. The method of any one of clauses 1-13, further comprising: enabling, by the keyboard application, at least one third decoder in response to determining at least one other target language of the text. 
     Clause 14. The method of any one of clauses 1-13, wherein enabling the second decoder comprises: outputting, by the keyboard application, for display, a graphical indication overlaying at least a portion of the graphical keyboard indicating that the second decoder is enabled. 
     Clause 15. A mobile device comprising: a presence-sensitive display component; at least one processor; and a memory that stores instructions for a keyboard application that, when executed, cause the at least one processor to: output, for display at the presence-sensitive display, a graphical keyboard; determine, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, determine, using a machine-learned model, the target language of the text; determine whether the target language is different than the language associated with the first decoder; if the target language of the text is not different than a language associated with the first decoder, output, for display at the presence-sensitive display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enable, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and output, for display at the presence-sensitive display, an indication of one or more second candidate words determined by the second decoder from the text. 
     Clause 16. The mobile device of clause 15, wherein the instructions, when executed, further cause the at least one processor to train the machine-learned model based on user inputs received by other keyboard applications executing at other computing devices, wherein the second decoder was enabled by at least some of the other keyboard applications while receiving the user inputs. 
     Clause 17. The mobile device of any one of clauses 15 or 16, wherein the characteristic of the text is a byte-length of the text or a minimum number of words associated with the text. 
     Clause 18. A computer-readable storage medium comprising instructions for a keyboard application that when executed cause at least one processor of a computing device to: output, for display, a graphical keyboard; determine, using a first decoder and based on a selection of one or more keys of the graphical keyboard, text; responsive to determining that a characteristic of the text satisfies a threshold for determining a target language of the text, determine, using a machine-learned model, the target language of the text; determine whether the target language is different than a language associated with the first decoder; if the target language of the text is not different than the language associated with the first decoder, output, for display, an indication of one or more first candidate words determined by the first decoder from the text; and if the target language of the text is different than the language associated with the first decoder: enable, a second decoder, wherein a language associated with the second decoder matches the target language of the text; and output, for display, an indication of one or more second candidate words determined by the second decoder from the text. 
     Clause 19. The computer-readable storage medium of clause 18, wherein the instructions, when executed, further cause the at least one processor to train the machine-learned model based on user inputs received by other keyboard applications executing at other computing devices, wherein the second decoder was enabled by at least some of the other keyboard applications while receiving the user inputs. 
     Clause 20. The computer-readable storage medium of any one of clauses 18 or 19, wherein the characteristic of the text is a byte-length of the text or a minimum number of words associated with the text. 
     Clause 21. A system comprising means for performing any of the methods of clauses 1-14. 
     Clause 22. A computing device comprising means for performing any of the methods of clauses 1-14. 
     In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof If implemented in software, the functions may be stored on or transmitted over, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media, which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium. 
     By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are instead directed to non-transient, tangible storage media. Disk and disc, as used, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described. In addition, in some aspects, the functionality described may be provided within dedicated hardware and/or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements. 
     The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware. 
     Various examples have been described. These and other examples are within the scope of the following claims.