Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

A task comprising content specified in a first language at a first location associated with an organization is received at a task application associated with a second location of the organization. The task specifies work to be performed at the second location associated with the organization. In response to determining that the first language is different from a second language preferred by a task recipient at the second location of the organization, translation of at least a portion of content of the task from the first language into the second language is automatically obtained by the task application. At least the portion of the content of the task translated to the second language is then displayed to the task recipient in a user interface of the task application.

A task comprising content specified in a first language at a first location associated with an organization is received at a task application associated with a second location of the organization. The task specifies work to be performed at the second location associated with the organization. In response to determining that the first language is different from a second language preferred by a task recipient at the second location of the organization, translation of at least a portion of content of the task from the first language into the second language is automatically obtained by the task application. At least the portion of the content of the task translated to the second language is then displayed to the task recipient in a user interface of the task application.

A system for classifying a language sample intent by receiving a language sample including a set of features, identifying language sample features, determining a tokenization score for the language sample according to the language sample features, eliminating duplicate features according to the tokenization score, determining a term frequency (tf) according to the identified features and the tokenization score, determining an inverse document frequency (idf) according to the identified features and the tokenization score, and generating a term frequency-inverse document frequency (tf-idf) matrix for the identified features.

A computer-executable method for moderating publication of data content with a moderator tool. The data contents are labelled as acceptable or unacceptable. The moderator tool receives the training data and executes a first algorithm that identifies features that exist in the training data and extracts them and ending up with a feature space. The moderator tool executes a second algorithm in the feature space for defining a distribution of data features that differentiate between the acceptable contents and the unacceptable contents in order to create a moderation model. When the moderator tool receives a new data content to be moderated, it executes the moderator tool on the new data content for identifying the data features in the new data content to be moderated in accordance with the moderation model created, and for producing a moderation result for the new data content by indicating whether the new data content is acceptable.

In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature. Based on a similarity score of the first and second feature vectors at least meeting a similarity score threshold, the Listing Engine generates a pairing of the first listing in the first language with the second listing in the second language for inclusion in training data of a machine translation system.

A method, computer program product, and a system where a processor(s), monitors multilingual switches performed on a client on behalf of a given user. Based on the monitoring, the processor(s) identifies switch patterns of the given user to generate a service profile for the user of machine learned multilingual switch patterns for the given user. The processor(s) determines a priority order for languages comprising the voice input streams, for the given user. The processor(s) obtains a new translation request initiated by the client, on behalf of the given user and applies the priority order to identify one or more languages spoken in a voice input stream of the new translation request. The processor(s) transmits indicators of the identified one or more languages to the client, where upon receiving the indicators, the client translates the voice input stream from the identified one or more languages to one or more target languages.

A method, computer program product, and a system where a processor(s), monitors multilingual switches performed on a client on behalf of a given user. Based on the monitoring, the processor(s) identifies switch patterns of the given user to generate a service profile for the user of machine learned multilingual switch patterns for the given user. The processor(s) determines a priority order for languages comprising the voice input streams, for the given user. The processor(s) obtains a new translation request initiated by the client, on behalf of the given user and applies the priority order to identify one or more languages spoken in a voice input stream of the new translation request. The processor(s) transmits indicators of the identified one or more languages to the client, where upon receiving the indicators, the client translates the voice input stream from the identified one or more languages to one or more target languages.

A system and method for detecting global interpretation risks in an email. A method includes extracting data from a header of an email, the data indicative of a locale of a sender; comparing the extracted data to other data indicative of a locale of a recipient of the email; and in response to a mismatch between the locales of the sender and recipient: scanning content of the email to identify portions of content subject to misinterpretation by the recipient, and modifying display of the email in response to an identification of at least one portion of content subject to misinterpretation, the modifying to indicate possible misinterpretation of the content by the recipient.

A method, computer system, and computer program product for multi-lingual chatlog training are provided. The embodiment may include receiving, by a processor, a plurality of data related to conversational data in multiple languages. The embodiment may also include assigning an intent label to each conversational data. The embodiment may further include assigning a language label to each conversational data. The embodiment may also include paring the plurality of the data related to the conversational data according to the intent label and the language label. The embodiment may further include training a machine learning model using a multi-lingual and multi-intent conversational data pairing. The embodiment may also include training the machine learning model using a single language and multi-intent conversational data paring.