Various embodiments disclosed relate to a translation tool for use with a project management or docketing system. The present disclosure includes methods and systems for receiving, identifying, and translating one or more portions of a foreign language document, such as an image document.

Various embodiments disclosed relate to a translation tool for use with a project management or docketing system. The present disclosure includes methods and systems for receiving, identifying, and translating one or more portions of a foreign language document, such as an image document.

In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Systems and methods for profile-based language translation and filtration are provided. A user language profile specifying one or more translation rules may be stored in memory for a user. A current communication session associated with a user device of the user may be monitored. The current communication session may includes messages from one or more other user devices of one or more other users. A language set in at least one of the messages of the current communication session may be detected as triggering at least one of the translation rules in real-time. The language set in the at least one message may further be filtered in real-time based on the at least one translation rule, which may thereby modify the at least one message. Further, a presentation of the current communication session that is provided to the user device may be modified to include the filtered language set of the modified message instead of the triggering language set.

Techniques are disclosed for determining a target language for a communication session and configuring a language mode of an input method editor (IME) to the target language. An example methodology implementing the techniques includes, by a computing device, detecting a communication to a recipient via a software application running on the computing device, determining a target language for the communication, and configuring a language mode of an input method editor to the target language. The target language may be determined based on an attribute or attributes of the recipient of the communication. In some cases, the target language may be determined based on an attribute or attributes of a contents of a prior communication.

Briefly, embodiments of a system, method, and article for receiving a document from a remote device and identifying items in the document. Various operations may be performed based on one or more dependencies of the identified items. For example, additional items may be identified in the document. One or more of the identified items may be parsed. A correspondence between the identified items and a second set of items may be determined. The identified items may be validated based on a set of rules. One or more of the identified items may be transmitted to the remote device in response to the performance of the various operations.

Briefly, embodiments of a system, method, and article for receiving a document from a remote device and identifying items in the document. Various operations may be performed based on one or more dependencies of the identified items. For example, additional items may be identified in the document. One or more of the identified items may be parsed. A correspondence between the identified items and a second set of items may be determined. The identified items may be validated based on a set of rules. One or more of the identified items may be transmitted to the remote device in response to the performance of the various operations.

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

A server accesses a natural language query corresponding to one of a plurality of natural languages. The server maps, using a query-to-vector engine configured to leverage word embeddings in each of the plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries, the natural language query to a vector. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.

A server accesses a natural language query corresponding to one of a plurality of natural languages. The server maps, using a query-to-vector engine configured to leverage word embeddings in each of the plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries, the natural language query to a vector. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.