Language proficiency inference system

Disclosed are systems, methods, and non-transitory computer-readable media for a language proficiency inference system used to determine a user's proficiency in one or more languages. The language proficiency inference system determines both text-based probability scores and profile-based probability scores indicating a probability that a user speaks a language or set of languages. The text-based probability score is based on text associated with the first user, whereas the profile-based probability score is based profile data of the user. The language proficiency inference system determines aggregated probability scores based on the corresponding text-based and profile-based probability scores. For example, the aggregated probability score is the sum of the text and profile-based probability scores. The language proficiency inference system uses the aggregated scores to determine the languages in which the user is proficient.

TECHNICAL FIELD

An embodiment of the present subject matter relates generally to data inference and, more specifically, to language proficiency inference.

BACKGROUND

Many online services support multiple languages. For example, a content provider may maintain a copy of the same content in English and Spanish and present the appropriate version of the content to a user based on the user's native language. As another example, a recommendation system may generate recommended responses for a user in the user's native language. Determining the user's language proficiency is therefore vital to the functionality of these systems, however is not always an easy task. The data available for a user may be limited, and therefore inadequate to properly determine the user's language proficiency. For example, many users have incomplete profile data that describes the user. Further, user behavior is often noisy and unreliable to properly determine a user's language proficiency. For example, a user may click an article in a foreign language simply to view the images, rather than read the text. Accordingly, improvements are needed.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, various details are set forth in order to provide a thorough understanding of some example embodiments. It will be apparent, however, to one skilled in the art, that the present subject matter may be practiced without these specific details, or with slight alterations.

Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present subject matter. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” appearing in various places throughout the specification are not necessarily all referring to the same embodiment.

For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the present subject matter. However, it will be apparent to one of ordinary skill in the art that embodiments of the subject matter described may be practiced without the specific details presented herein, or in various combinations, as described herein. Furthermore, well-known features may be omitted or simplified in order not to obscure the described embodiments. Various examples may be given throughout this description. These are merely descriptions of specific embodiments. The scope or meaning of the claims is not limited to the examples given.

Disclosed are systems, methods, and non-transitory computer-readable media for a language proficiency inference system used to determine a user's proficiency in one or more languages. The user's proficiency in a language indicates how well the user speaks and/or understands the language. The language proficiency inference system uses both a user's profile data, as well as text associated with the user, to determine the user's proficiency in a language. The user's profile data includes data describing the user, such as the user's address, nationality, employer, education history, etc. The text associated with the user includes text that was authored and/or viewed by the user.

Previous approaches for determining a user's language proficiency depend on counters. Specifically, previous approaches count the number of times a user engages with text in a particular language as determined by a language classifier. The user's proficiency in a language is then determined based on the determined count for the particular language. While simple, this approach fails if the language classifier is sufficiently noisy. Further, this approach is only suitable for finding either a single spoken language or a distribution over languages, although the results in this regard are questionable in quality. The previous approach is not, however, suitable for determining a set of languages spoken by a user.

The language proficiency inference system alleviates these prior shortcomings. Rather than relying simply on a counter, the language proficiency inference system determines both a text-based probability score that a user speaks a given language or given set of languages and a profile-based probability score that the user speaks the given language or the given set of languages. The text-based probability score is determined based on the text associated with the user, while the profile-based probability score is based on the user's profile data. The language proficiency inference system determines an aggregated probability score that a user speaks a given language or set of given languages based on the text-based probability score and the profile-based probability scores. The language proficiency inference system uses the aggregated probability scores to infer the user's proficiency in each language. For example, the language proficiency inference system infers that the language or set of languages for which the user has the highest aggregated probability score is the user's primary language(s). By utilizing multiple data signals and determining probability scores for sets of languages, the language proficiency inference system more accurately gauges the number of languages a user is likely to speak, as well as the user's proficiency in those specific languages.

The user's inferred language proficiency may be used for several purposes. For example, the user's inferred language proficiency may be used to select content to present to the user. That is, the content selected for the user may be in the user's inferred primary language. As another example, the user's inferred language proficiency may be use when generating recommended messages for the user. A messaging system may provide users with recommended responses that the user may use to respond to a received message. The messaging system may use the user's inferred language proficiency to determine the language in which to generate the recommended responses.

FIG. 1shows an example system100, wherein electronic devices communicate via a network for purposes of exchanging content and other data. As shown, multiple devices (i.e., client device102, client device104, online service106, and language proficiency inference system108) are connected to a communication network110and configured to communicate with each other through use of the communication network110. The communication network110is any type of network, including a local area network (LAN), such as an intranet, a wide area network (WAN), such as the interne, or any combination thereof. Further, the communication network110may be a public network, a private network, or a combination thereof. The communication network110is implemented using any number of communications links associated with one or more service providers, including one or more wired communication links, one or more wireless communication links, or any combination thereof. Additionally, the communication network110is configured to support the transmission of data formatted using any number of protocols.

Multiple computing devices can be connected to the communication network110. A computing device is any type of general computing device capable of network communication with other computing devices. For example, a computing device can be a personal computing device such as a desktop or workstation, a business server, or a portable computing device, such as a laptop, smart phone, or a tablet personal computer (PC). A computing device can include some or all of the features, components, and peripherals of the machine700shown inFIG. 7.

To facilitate communication with other computing devices, a computing device includes a communication interface configured to receive a communication, such as a request, data, and the like, from another computing device in network communication with the computing device and pass the communication along to an appropriate module running on the computing device. The communication interface also sends a communication to another computing device in network communication with the computing device.

In the system100, users interact with the online service106to utilize the services provided by the online service106. The online service106may provide any type of service, such as a social networking service, online retail service, messaging service, etc. For example, the online service16may provide messaging functionality that enables users to establish and participate in communication sessions with each other. Users may the client devices102and104that are connected to the communication network110by direct and/or indirect communication to communicate with and utilize the functionality of the online service106.

Although the shown system100includes only two client devices102,104, this is only for ease of explanation and is not meant to be limiting. One skilled in the art would appreciate that the system100can include any number of client devices102,104. Further, the online service106may concurrently accept connections from and interact with any number of client devices102,104. The online service106supports connections from a variety of different types of client devices102,104, such as desktop computers; mobile computers; mobile communications devices, e.g., mobile phones, smart phones, tablets; smart televisions; set-top boxes; and/or any other network enabled computing devices. Hence, the client devices102and104may be of varying type, capabilities, operating systems, and so forth.

A user interacts with the online service106via a client-side application installed on the client devices102and104. In some embodiments, the client-side application includes a component specific to the online service106. For example, the component may be a stand-alone application, one or more application plug-ins, and/or a browser extension. However, the users may also interact with the online service106via a third-party application, such as a web browser, that resides on the client devices102and104and is configured to communicate with the online service106. In either case, the client-side application presents a user interface (UI) for the user to interact with the online service106. For example, the user interacts with the online service106via a client-side application integrated with the file system or via a webpage displayed using a web browser application.

The online service106is one or more computing devices configured to provide one or more services. For example, the online service106may be a messaging service that facilitates and manages communication sessions between various client devices102,104. As another example, the online service106may be a social networking service that allows users to share content with other members of the social networking service as well as view content posted by other members of the social networking service.

As part of its provided service, the online service106may select or generate content for a user and present the content to the user. For example, a social networking service may select content to be presented to a user in the user's feed on the social networking service. As another example, a messaging service may generate recommended response messages for a user that the user may use to respond to another user during a communication session (e.g., email communication session, instant messaging session, etc.).

To provide a user with relevant content, the online service106may select and/or generate content for a user based on the user's inferred proficiency in one or more languages. A user's inferred proficiency in a language indicates an estimated level at which the user can understand and/or use the given language. The online service106uses the user's inferred proficiency in one or more languages to select and/or generate content for a user in a language in which the user has sufficient proficiency. For example, the online service106may have access to multiple versions of an article that are each in a different language. The online service106may select a version of the content item to present to the user based on the user's proficiency in each available language of the article, such that the user is presented with the article in the language in which the user is most proficient.

The language proficiency inference system108determines a user's language proficiency in a given language or a set of languages. The language proficiency inference system108is shown as being separate from the online service106, however this is just one example and is not meant to be limiting. The language proficiency inference system108may also be incorporated as part of the online service106or as part of another online service (not shown)

The language proficiency inference system108determines the language proficiency of a user in a given language or set of languages based on both a user's profile data and as text associated with the user. The user's profile data includes data describing the user, such as the user's address, nationality, employer, education history, etc. The text associated with the user includes text that the user has engaged with. For example, the text associated with the user included text that was authored, viewed, liked, commented on, etc., by the user. The online service106may maintain a user profile for users of the online service106, which includes both the user profile data as well as user interaction data that indicates content that a user has engaged with (e.g., read, authored, etc.) while using the online service106.

The language proficiency inference system108determines both a text-based probability score that a user speaks a given language or set of languages and a profile-based probability score that the user speaks the given language or set of languages. The text-based probability score is determined based on the text associated with the user, while the profile-based probability score is based on the user's profile data. The language proficiency inference system108determines an aggregated probability score that a user speaks a given language or set of languages based on the text-based probability score and the profile-based probability score.

The probability scores, either the text-based, profile-based, or aggregated probability scores, may be a numeric value indicating a probability, or alternatively, another type of value that is based on the determined probability. For instance, a probability score may be a score indicating the relative probability, however not itself representing the actual probability value. As another example, the probability score may represent a ranking relative to the other scores. Accordingly, the highest probability score would be represented as 1, rather than the percentage of probability.

The language proficiency inference system108uses the aggregated probability scores to infer the user's proficiency in each language and set of languages. For example, the language proficiency inference system108infers that the language for which the user has the highest aggregated probability score is the user's primary language. Likewise, the language proficiency inference system108may determine that a user is not proficient in a given language if the user's aggregated probability score for the given language is below a predetermined threshold.

FIG. 2is a block diagram of the language proficiency inference system108, according to some example embodiments. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components (e.g., modules) that are not germane to conveying an understanding of the inventive subject matter have been omitted fromFIG. 2. However, a skilled artisan will readily recognize that various additional functional components may be supported by the language proficiency inference system108to facilitate additional functionality that is not specifically described herein. Furthermore, the various functional modules depicted inFIG. 2may reside on a single computing device or may be distributed across several computing devices in various arrangements such as those used in cloud-based architectures. For example, the various functional modules and components may be distributed amongst computing devices that facilitate both the language proficiency inference system108and the online service106.

As shown, the language proficiency inference system108includes a data gathering module202, a text-based probability score determination module204, a profile-based probability score determination module206, an aggregated probability score determination module208, a language proficiency determination module210, an output module212, and a data storage214.

The data gathering module202gathers data used by the language proficiency inference system110to determine a user's proficiency in one or more languages. Specifically, the data gathering module202gathers data from the data storage214. The data storage214maintains user profile data for users of the online service106. Although the data storage214is shown as being a part of the language proficiency inference system110, it may alternatively be a part of the online service106. In this type of embodiments, the data gathering module202communicates with the online service106to access the data storage214.

The user profile data stored in the data storage214includes profile data describing a user, such as the user's address, nationality, employer, education history, etc., as well as interaction data describing text associated with the user. That is, the interaction data indicates text that the user has engaged with by, for example, authoring the text, reading the text, liking the text, providing feedback to the text, clicking on the text, etc.

The profile data stored in the data storage214is associated with a unique user identifier assigned to the corresponding user. The data gathering module202uses the unique user identifier for a user to identify the corresponding user profile data in the data storage214. Once the data gathering module202has identified the user profile associated with the unique identifier, the data gathering module202gathers the user profile data, including the data describing the user and the user's interaction data.

The text-based probability score determination module204determines text-based probability scores that a user speaks a given language or a set of given languages. The text-based probability score determination module204determines the text-based probability scores based on text associated with the user, such as text that the user has authored, read, clicked, liked, etc. For example, determined incidences of the user engaging with a given language increases the probability that the user speaks the language. The text-based probability score determination module204can use any type of suitable model or algorithm to determine the text-based probability scores that a user speaks a given language or set of languages. In some embodiments, the text-based probability score determination module204applies different weights to text that user has engaged with based on the type of engagement and/or text. For example, the text-based probability score determination module204may add additional weight to texts written by the user, and less weight to texts that were read by the user. In some embodiments, the text-based probability score determination module204may apply additional weights to text-based on the length of the engagement. For example, the text-based probability score determination module204may give more weight to text that the user spent a longer time reading or otherwise engaging with, and less weight to text that the user read or engaged with for just a short period of time.

The text-based probability score determination module204may determine scores for individual pieces of text and use these individual scores to calculate the final text-based probability scores. As explained previously, the text-based probability score determination module204may apply varying weights to the individual scores based on the type of text and/or the type or duration of the engagement. The text-based probability score determination module204may utilize a minimum score for a text-based probability score, such that a text-based probability score is not a zero.

In some embodiments, the text-based probability score determination module204determines a text-based probability score for each of a set of languages, as well as all possible combination of the set of languages. For example, assuming the set of languages includes English and French, the text-based probability score determination module204determines a text-probability scores that the user speaks English, a text-probability scores that the user speaks French, and a text-probability scores that the user speaks both English and French. Likewise, if the set of languages includes English, French and Spanish, the text-based probability score determination module204determines a text-probability scores that the user speaks English, a text-probability scores that the user speaks French, a text-probability scores that the user that the user speaks Spanish, a text-probability scores that the user speaks English and French, a text-probability scores that the user speaks English and Spanish, a text-probability scores that the user speaks French and Spanish, and a text-probability scores that the user speaks English, French and Spanish.

The profile-based probability score determination module206determines profile-based probability scores that a user speaks a given language or a set of given languages. In contrast to a text-based probability score, the profile-based probability score determination module206determines the profile-based probability scores based on user profile data of the user, such as the user's address, nationality, employment history, etc. The profile-based probability score determination module206may determine the profile-based probability score in a two-part process. First, the profile-based probability score determination module206may use the profile data to determine probability scores indicating the number of languages the user speaks. For example, many Europeans speak three or more languages, whereas Americans often speak only one or two languages. Additionally, the profile-based probability score determination module206may use profile data to determine probability scores that the user speaks a given language or set of languages. For example, the profile-based probability score determination module206may use the user's nationality and residence to infer the languages that user is proficient in. The profile-based probability score determination module206combines these values to determine that profile-based probability scores that the user speaks a given language or set of languages. That is the profile-based probability score determination module206uses the probability scores indicating the number of languages the user speaks and the probability scores that the user speaks a given language or set of languages to determine the final profile-based probability scores. The profile-based probability score determination module206can use any type of suitable model or algorithm to determine the profile-based probability scores that a user speaks a given language or set of languages.

The profile-based probability score determination module206determines profile-based probability scores for the same set of languages and/or combination of languages for which the text-based probability score determination module204determined text-based probability scores. As a result, a text-based probability score and profile-based probability score is determined for each individual language and/or set of languages.

The aggregated probability score determination module208determines aggregated probability scores that a user a speaks a given language or a set of given languages. The aggregated probability score determination module208determines the aggregated probability scores that a user a speaks a given language or a set of given languages based on the text-based probability score and the profile-based probability scores that the user speaks the given language or set of given language. For example, the aggregated probability score determination module208determines the aggregated probability score that a user speaks Spanish based on the text-based probability score that the user speaks Spanish and the profile-based probability score that the user speaks Spanish. As another example, the aggregated probability score determination module208determines that aggregated probability score that a user speaks Spanish and English based on the text-based probability score that the user speaks Spanish and English, and the profile-based probability score that the user speaks Spanish and English.

The aggregated probability score determination module208can determine the aggregated probability score in any number of ways. For example, in some embodiments, the aggregated probability score determination module208determines an aggregated probability score by summing or multiplying the corresponding text-based probability score and profile-based probability score. For example, the aggregated probability score determination module208determines the aggregated probability score that a user speaks Spanish by summing the text-based probability score that the user speaks Spanish with the profile-based probability score that the user speaks Spanish. This is just one example, however, and is not meant to be limiting. The aggregated probability score determination module208may use any suitable formula to determine the aggregated probability score based on the corresponding text-based probability score and profile-based probability score. For example, in some embodiments, the aggregated probability score determination module208maximizes the text-based probability score and the profile-based probability score using an algorithm such as the following:

The language proficiency determination module210determines a user's proficiency in a language or set of languages based on the aggregated probability score that the user speaks the language or set of languages. For example, the language proficiency determination module210compares the aggregated probability scores to each other the determine the highest aggregated probability score. The language proficiency determination module210then determines that the user is proficient in the language or set of languages associated with the highest aggregated probability score. For example, the language proficiency determination module210may compare the aggregated probability score that the user speaks either Spanish, English, or the set of Spanish and English, and determines that the user is proficient in the language or set of languages that has the highest aggregated probability score. That is, the language proficiency determination module210determines that the user is only proficient in Spanish if the aggregated probability score for Spanish is the highest, the user is only proficient in English if the aggregated probability score for English is the highest, or the user is proficient in both Spanish and English if the aggregated probability score for the set of Spanish and English is the highest.

In the event that the language proficiency determination module210determines that a user is proficient in a set of 2 or more languages, the language proficiency determination module210may determine an order of proficiency of the user in the languages. That is, the language proficiency determination module210may determine the language that is the user's primary language, secondary language, tertiary language, etc. The language proficiency determination module210may determine the order of proficiency in a number of ways. For example, the language proficiency determination module210may use the aggregated probability scores for each language in the set to determine the order of proficiency. That is, if the language proficiency determination module210determines that the user is proficient in both Spanish and English, the language proficiency determination module210uses the aggregated probability score that the user speaks Spanish only and the aggregated probability score that the user speaks English only to determine which language is the user's primary language and which language is the user's secondary language. The language proficiency determination module210determines that the language that has the higher aggregated probability score is the user's primary language.

As another example, the language proficiency determination module210may use the user's region to determine the user's primary language. For example, the language proficiency determination module210may determine that the language commonly spoken in the geographic area that the user currently lives in, lived in the longest, or grew up in is the user's primary language.

In some embodiments, the language proficiency determination module210may use a known common order in which languages are learned to deter mine the proficiency order of the languages. For example, it may be common for a user to initially learn English, followed by Spanish and then French. Accordingly, the language proficiency determination module210may determine the user's proficiency order based on this known order in which languages are commonly learned.

The output module212updates the user's profile in the data storage214to reflect the user's determined language proficiency. This may include updating the user's profile to indicate the languages that the user is proficient in, as well as the proficiency order of the languages. For example, the output module212may update the user's profile to indicate the user's primary language, secondary language, tertiary language, etc. In sonic embodiments, the output module212may also update the user's profile to reflect the languages in which the user is not proficient. For example, the output module212may update the user's profile to indicate that the user is not proficient in languages for which the corresponding aggregated probability score is below a threshold score.

The online service106may use the user's language proficiency for a variety of uses, such as when generating and or selecting content for the user. For example, the online service106may utilize the user's determined language proficiency in determining a version of a content item to present to the user. The online service106may maintain multiple versions of a content item that are in different languages. When selecting which version of the content item to present to a user, the online service106may determine from the user's profile in the data storage214the languages in which the user is proficient and select a content item accordingly. As another example, the online service106may use the user's language proficiency to select a language in which to generate a recommended message response for the user. These are only a couple of examples of how the online service106may use a user's language proficiency, and are not meant to be limiting.

FIG. 3is a flowchart showing an example method300of determining a user's proficiency in one or more languages, according to certain example embodiments. The method300may be embodied in computer readable instructions for execution by one or more processors such that the operations of the method300may be performed in part or in whole by the language proficiency inference system110; accordingly, the method300is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method300may be deployed on various other hardware configurations and the method300is not intended to be limited to the language proficiency inference system110.

At operation302, the data gathering module202gathers user profile data and user interaction data. The data gathering module202gathers data used by the language proficiency inference system110to determine a user's proficiency in one or more languages. Specifically, the data gathering module202gathers data from the data storage214. The data storage214maintains user profile data for users of the online service106. Although the data storage214is shown as being a part of the language proficiency inference system110, it may alternatively be a part of the online service106. In this type of embodiments, the data gathering module202communicates with the online service106to access the data storage214.

The user profile data stored in the data storage214includes profile data describing a user, such as the user's address, nationality, employer, education history, etc., as well as interaction data describing text associated with the user. That is, the interaction data indicates text that the user has engaged with by, for example, authoring the text, reading the text, liking the text, providing feedback to the text, clicking on the text, etc.

The profile data stored in the data storage214is associated with a unique user identifier assigned to the corresponding user. The data gathering module202uses the unique user identifier for a user to identify the corresponding user profile data in the data storage214. Once the data gathering module202has identified the user profile associated with the unique identifier, the data gathering module202gathers the user profile data, including the data describing the user and the user's interaction data.

At operation304, the text-based probability determination module204determines text-based probability scores. The text-based probability scores indicate a probability that a user speaks a given language or a set of given languages. The text-based probability score determination module204determines the text-based probability scores based on text associated with the user, such as text that the user has authored, read, clicked, liked, etc. For example, determined incidences of the user engaging with a given language increases the probability that the user speaks the language. The text-based probability score determination module204can use any type of suitable model or algorithm to determine the text-based probability scores that a user speaks a given language or set of languages. In some embodiments, the text-based probability score determination module204applies different weights to text that user has engaged with based on the type of engagement and/or text. For example, the text-based probability score determination module204may add additional weight to texts written by the user, and less weight to texts that were read by the user. In some embodiments, the text-based probability score determination module204may apply additional weights to text-based on the length of the engagement. For example, the text-based probability score determination module204may give more weight to text that the user spent a longer time reading or otherwise engaging with, and less weight to text that the user read or engaged with for just a short period of time.

The text-based probability score determination module204may determined scores for individual pieces of text and use these individual scores to calculate the final text-based probability scores. As explained previously, the text-based probability score determination module204may apply varying weights to the individual scores based on the type of text and/or the type or duration of the engagement. The text-based probability score determination module204may utilize a minimum score for a text-based probability score, such that a text-based probability score is not a 0.

In some embodiments, the text-based probability score determination module204determines a text-based probability score for each of a set of languages, as well as all possible combination of the set of languages. For example, assuming the set of languages includes English and French, the text-based probability score determination module204determines a text-probability scores that the user speaks English, a text-probability scores that the user speaks French, and a text-probability scores that the user speaks both English and French. Likewise, if the set of languages includes English, French and Spanish, the text-based probability score determination module204determines a text-probability scores that the user speaks English, a text-probability scores that the user speaks French, a text-probability scores that the user that the user speaks Spanish, a text-probability scores that the user speaks English and French, a text-probability scores that the user speaks English and Spanish, a text-probability scores that the user speaks French and Spanish, and a text-probability scores that the user speaks English, French and Spanish.

At operation306, the profile-based probability score determination module206determines profile-based probability scores. The profile-based probability scores indicate a probability that a user speaks a given language or a set of given languages. In contrast to a text-based probability score, the profile-based probability score determination module206determines the profile-based probability scores based on user profile data of the user, such as the user's address, nationality, employment history, etc. The profile-based probability score determination module206may determine the profile-based probability score in a two-part process. First, the profile-based probability score determination module206may use the profile data to determine probability scores indicating the number of languages the user speaks. For example, many Europeans speak 3 or more languages, whereas Americans often speak only 1 to 2 language. Additionally, the profile-based probability score determination module206may use profile data to determine probability scores that the user speaks a given language or set of languages. For example, the profile-based probability score determination module206may use the user's nationality and residence to infer the languages that user is proficient in. The profile-based probability score determination module206combines these values to determine that profile-based probability scores that the user speaks a given language or set of languages. That is the profile-based probability score determination module206uses the probability scores indicating the number of languages the user speaks and the probability scores that the user speaks a given language or set of languages to determine the final profile-based probability scores. The profile-based probability score determination module206can use any type of suitable model or algorithm to determine the profile-based probability scores that a user speaks a given language or set of languages.

The profile-based probability score determination module206determines profile-based probability scores for the same set of languages and/or combination of languages for which the text-based probability score determination module204determined text-based probability scores. As a result, a text-based probability score and profile-based probability score is determined for each individual language and/or set of languages.

At operation308, the aggregated probability score determination module208determines aggregated probability scores. The aggregated probability score determination module208determines the aggregated probability scores that a user a speaks a given language or a set of given languages based on the text-based probability score and the profile-based probability scores that the user speaks the given language or set of given language. For example, the aggregated probability score determination module208determines the aggregated probability score that a user speaks Spanish based on the text-based probability score that the user speaks Spanish and the profile-based probability score that the user speaks Spanish. As another example, the aggregated probability score determination module208determines that aggregated probability score that a user speaks Spanish and English based on the text-based probability score that the user speaks Spanish and English, and the profile-based probability score that the user speaks Spanish and English.

The aggregated probability score determination module208can determine the aggregated probability score in any number of ways. For example, in some embodiments, the aggregated probability score determination module208determines an aggregated probability score by summing the corresponding text-based probability score and profile-based probability score. For example, the aggregated probability score determination module208determines the aggregated probability score that a user speaks Spanish by summing the text-based probability score that the user speaks Spanish with the profile-based probability score that the user speaks Spanish. This is just one example, however, and is not meant to be limiting. The aggregated probability score determination module208may use any suitable formula to determine the aggregated probability score based on the corresponding text-based probability score and profile-based probability score.

At operation310, the language proficiency determination module210determines a user's proficiency in a language or set of languages based on the aggregated probability score that the user speaks the language or set of languages. For example, the language proficiency determination module210compares the aggregated probability scores to each other the determine the highest aggregated probability score. The language proficiency determination module210then determines that the user is proficient in the language or set of languages associated with the highest aggregated probability score. For example, the language proficiency determination module210may compare the aggregated probability score that the user speaks either Spanish, English, or the set of Spanish and English, and determines that the user is proficient in the language or set of languages that has the highest aggregated probability score. That is, the language proficiency determination module210determines that the user is only proficient in Spanish if the aggregated probability score for Spanish is the highest, the user is only proficient in English if the aggregated probability score for English is the highest, or the user is proficient in both Spanish and English if the aggregated probability score for the set of Spanish and English is the highest.

In the event that the language proficiency determination module210determines that a user is proficient in a set of 2 or more languages, the language proficiency determination module210may determine an order of proficiency of the user in the languages. That is, the language proficiency determination module210may determine the language that is the user's primary language, secondary language, tertiary language, etc. The language proficiency determination module210may determine the order of proficiency in a number of ways. For example, the language proficiency determination module210may use the aggregated probability scores for each language in the set to determine the order of proficiency. That is, if the language proficiency determination module210determines that the user is proficient in both Spanish and English, the language proficiency determination module210uses the aggregated probability score that the user speaks Spanish only and the aggregated probability score that the user speaks English only to determine which language is the user's primary language and which language is the user's secondary language. The language proficiency determination module210determines that the language that has the higher aggregated probability score is the user's primary language.

As another example, the language proficiency determination module210may use the user's region to determine the user's primary language. For example, the language proficiency determination module210may determine that the language commonly spoken in the geographic area that the user currently lives in, lived in the longest, or grew up in is the user's primary language.

In some embodiments, the language proficiency determination module210may use a known common order in which languages are learned to determine the proficiency order of the languages. For example, it may be common for a user to initially learn English, followed by Spanish and then French. Accordingly, the language proficiency determination module210may determine the user's proficiency order based on this known order in which languages are commonly learned.

At operation312, the output module212updates the user's profile in the data storage214to reflect the user's determined language proficiency. This may include updating the user's profile to indicate the languages that the user is proficient in, as well as the proficiency order of the languages. For example, the output module212may update the user's profile to indicate the user's primary language, secondary language, tertiary language, etc. In some embodiments, the output module212may also update the user's profile to reflect the languages in which the user is not proficient. For example, the output module212may update the user's profile to indicate that the user is not proficient in languages for which the corresponding aggregated probability score is below a threshold score.

The online service106may use the user's language proficiency for a variety of uses, such as when generating and or selecting content for the user. For example, the online service106may utilize the user's determined language proficiency in determining a version of a content item to present to the user. The online service106may maintain multiple versions of a content item that are in different languages. When selecting which version of the content item to present to a user, the online service106may determine from the user's profile in the data storage214the languages in which the user is proficient and select a content item accordingly. As another example, the online service106may use the user's language proficiency to select a language in which to generate a recommended message response for the user. These are only a couple of examples of how the online service106may use a user's language proficiency, and are not meant to be limiting.

FIG. 4is a flowchart showing an example method400of selecting content based on a user's language proficiency, according to certain example embodiments. The method400may be embodied in computer readable instructions for execution by one or more processors such that the operations of the method400may be performed in part or in whole by the online service106; accordingly, the method400is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method400may be deployed on various other hardware configurations and the method400is not intended to be limited to the online service106.

At operation402, the online service106receives a request to present a user with a content item. For example, the online service106may be a social networking service and the request may be transmitted as a result of the user logging into their account. As another example, the online service106may be a news site and the request may be transmitted as a result of a user selecting to view an article.

At operation404, the online service106identifies the user language proficiency from the user's profile. For example, the online service106accesses the user's profile in the data storage214and gathers the users stored language proficiency. The user's language proficiency may have been determined and updated by the language proficiency inference system110.

At operation406, the online service106selects a version of the content item based on the user's language proficiency. For example, the online service106selects a version of the content item that is written in the users primary or secondary language.

At operation408, the online service106presents the selected content item to the user. For example, the online service106transmits the content item to a client device102of the user, where the content item is then presented to the user by the client device102.

FIG. 5is a flowchart showing an example method500of generating recommended response messages based on a user's language proficiency, according to certain example embodiments. The method500may be embodied in computer readable instructions for execution by one or more processors such that the operations of the method500may be performed in part or in whole by the online service106; accordingly, the method500is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method500may be deployed on various other hardware configurations and the method500is not intended to be limited to the online service106.

At operation502, the online service106receives a message intended for a recipient user. For example, the online service106may be me a messaging service that facilitates communication sessions between multiple user client devices102,104. A communication allows users to transmit messages back and forth via the online service106. For example, messages transmitted from a first client device102to a second client device104are initially routed to the online service106. The online service106then forwards the message to its intended recipient client device104.

At operation504, the online service106identifies the user language proficiency from the user's profile. For example, the online service106accesses the user's profile in the data storage214and gathers the users stored language proficiency. The user's language proficiency may have been determined and updated by the language proficiency inference system110.

At operation506, the online service106generates a recommended response to the received message based on the user's language proficiency. For example, the online service106generates the recommended response in the users primary or secondary language.

At operation508, the online service106transmits the message and the recommended response to the recipient user's client device104. The client device104presents the message and the recommended response to the recipient user. The user may select the recommended response to quickly reply to the received message.

Software Architecture

FIG. 6is a block diagram illustrating an example software architecture606, which may be used in conjunction with various hardware architectures herein described.FIG. 6is a non-limiting example of a software architecture606and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture606may execute on hardware such as machine700ofFIG. 7that includes, among other things, processors704, memory714, and (input/output) I/O components718. A representative hardware layer652is illustrated and can represent, for example, the machine700ofFIG. 7. The representative hardware layer652includes a processing unit654having associated executable instructions604. Executable instructions604represent the executable instructions of the software architecture606, including implementation of the methods, components, and so forth described herein. The hardware layer652also includes memory and/or storage modules656, which also have executable instructions604. The hardware layer652may also comprise other hardware658.

In the example architecture ofFIG. 6, the software architecture606may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture606may include layers such as an operating system602, libraries620, frameworks/middleware618, applications616, and a presentation layer614. Operationally, the applications616and/or other components within the layers may invoke Application Programming Interface (API) calls608through the software stack and receive a response such as messages612in response to the API calls608. The layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware618, while others may provide such a layer. Other software architectures may include additional or different layers.

The operating system602may manage hardware resources and provide common services. The operating system602may include, for example, a kernel622, services624, and drivers626. The kernel622may act as an abstraction layer between the hardware and the other software layers. For example, the kernel622may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services624may provide other common services for the other software layers. The drivers626are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers626include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) divers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth, depending on the hardware configuration.

The libraries620provide a common infrastructure that is used by the applications616and/or other components and/or layers. The libraries620provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system602functionality (e.g., kernel622, services624, and/or drivers626). The libraries620may include system libraries644(e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, the libraries620may include API libraries646such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries620may also include a wide variety of other libraries648to provide many other APIs to the applications616and other software components/modules.

The frameworks/middleware618(also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications616and/or other software components/modules. For example, the frameworks/middleware618may provide various graphical user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware618may provide a broad spectrum of other APIs that may be used by the applications616and/or other software components/modules, some of which may be specific to a particular operating system602or platform.

The applications616include built-in applications638and/or third-party applications640. Examples of representative built-in applications638may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. Third-party applications640may include an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. The third-party applications640may invoke the API calls608provided by the mobile operating system (such as operating system602) to facilitate functionality described herein.

The applications616may use built in operating system functions (e.g., kernel622, services624, and/or drivers626), libraries620, and frameworks/middleware618to create UIs to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as presentation layer614. In these systems, the application/component “logic” can be separated from the aspects of the application/component that interact with a user.

The machine700may include processors704, memory/storage706, and I/O components718, which may be configured to communicate with each other such as via a bus702. The memory/storage706may include a memory714, such as a main memory, or other memory storage, and a storage unit716, both accessible to the processors704such as via the bus702. The storage unit716and memory714store the instructions710embodying any one or more of the methodologies or functions described herein. The instructions710may also reside, completely or partially, within the memory714, within the storage unit716, within at least one of the processors704(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine700. Accordingly, the memory714, the storage unit716, and the memory of processors704are examples of machine-readable media.

Communication may be implemented using a wide variety of technologies. The I/O components718may include communication components740operable to couple the machine700to a network732or devices720via coupling724and coupling722, respectively. For example, the communication components740may include a network interface component or other suitable device to interface with the network732. In further examples, communication components740may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices720may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that is capable of storing, encoding, or carrying instructions710for execution by the machine700, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions710. Instructions710may be transmitted or received over the network732using a transmission medium via a network interface device and using any one of a number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine700that interfaces to a communications network732to obtain resources from one or more server systems or other client devices. A client device102,104may be, but is not limited to, mobile phones, desktop computers, laptops, PDAs, smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, STBs, or any other communication device that a user may use to access a network732.

“MACHINE-READABLE MEDIUM” in this context refers to a component, device or other tangible media able to store instructions710and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions710. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions710(e.g., code) for execution by a machine700, such that the instructions710, when executed by one or more processors704of the machine700, cause the machine700to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se.

“PROCESSOR” in this context refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., “commands,” “op codes,” “machine code,” etc.) and which produces corresponding output signals that are applied to operate a machine700. A processor704may be, for example, a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, a radio-frequency integrated circuit (RFIC) or any combination thereof. A processor may further be a multi-core processor having two or more independent processors704(sometimes referred to as “cores”) that may execute instructions710contemporaneously.