Described herein are techniques for using a graph neural network to encode online job postings as embeddings. First, an input graph is defined by processing one or more rules to discover edges that connect nodes in an input graph, where the nodes of the input graph represent job postings or standardized job attributes, and the edges are determined based on analyzing a log of user activity directed to online job postings. Next, a graph neural network (GNN) is trained based on an edge prediction task. Finally, once trained, the GNN is used to derive node embeddings for the nodes (e.g., job postings) of the input graph, and in some instances, new online job postings not represented in the original input graph.

A set of candidate edits for a word of a sentence is obtained. Each of the set of candidate edits includes an edit word. Two or more surrounding words that each have a dependency relationship with the edit word are identified in the sentence. At least one of the two or more surrounding words is identified irrespective of their proximity to the edit word. The dependency relationship between the edit word and each of the surrounding words and the set of candidate edits is provided as input to a grammar accuracy prediction model. One or more outputs of the grammar accuracy prediction model are obtained. The one or more outputs indicate grammatical accuracy of each candidate edit from the set in the sentence in view of the dependency relationship with surrounding words. The candidate edit with highest accuracy is selected from the candidate edit set for the sentence.

A method and system for combining subsequent data in a communication stream including receiving an indication of a selection of a first image in a communication thread and identifying a user attribute setting associated with the first user. Based on one or more composite image generation rules, the method includes determining that the selected first image is compatible for converting into a composite image. The method further includes accessing a user attribute setting associated with a second user and generating the composite image based on the selected first image and the user attribute settings of the first and second users. The generated composite image is then caused to be displayed in the communication thread.

A non-transitory computer-readable storage medium may include instructions stored thereon for propagating changes to copied text. When executed by at least one processor, the instructions may be configured to cause a computing system to at least present copied text within a user interface of the computing system, monitor the user interface for changes to the copied text, receive a change to the copied text, the change including replacing a first instance of a first word, within the copied text, with a first instance of a second word, and in response to receiving the change to the copied text, present a prompt to replace, within the copied text, a second instance of the first word with a second instance of the second word.

A non-transitory computer-readable storage medium may include instructions stored thereon for propagating changes to copied text. When executed by at least one processor, the instructions may be configured to cause a computing system to at least present copied text within a user interface of the computing system, monitor the user interface for changes to the copied text, receive a change to the copied text, the change including replacing a first instance of a first word, within the copied text, with a first instance of a second word, and in response to receiving the change to the copied text, present a prompt to replace, within the copied text, a second instance of the first word with a second instance of the second word.

Systems and methods for facilitating an automated customization of user interface are disclosed. The system may include a processor including an AI engine and a Ul engine. The AI engine may receive an input data in the form of a pre-defined template that may facilitate the input data to be received in user-readable format. The AI engine may process the pre-defined template to extract input attributes corresponding to the input data. The AI engine may dynamically map the input attributes with sample attributes of a pre-stored file. Based on the dynamic mapping, the AI engine may obtain a form pertaining to an expected visual representation of the web page. The AI engine may facilitate responsive scaling of the form depending on size attributes of the user interface. The UI engine may include a form engine that may customize the form for generation of the web page.

Systems and methods for facilitating an automated customization of user interface are disclosed. The system may include a processor including an AI engine and a Ul engine. The AI engine may receive an input data in the form of a pre-defined template that may facilitate the input data to be received in user-readable format. The AI engine may process the pre-defined template to extract input attributes corresponding to the input data. The AI engine may dynamically map the input attributes with sample attributes of a pre-stored file. Based on the dynamic mapping, the AI engine may obtain a form pertaining to an expected visual representation of the web page. The AI engine may facilitate responsive scaling of the form depending on size attributes of the user interface. The UI engine may include a form engine that may customize the form for generation of the web page.

Systems, methods, and computer-executable instructions for synthesizing programs using a prefix of an output. A specification of a task to synthesize a program in a domain specific language (DSL) is received. The specification includes an input and a corresponding prefix of the output. Programs for the task are synthesized. The synthesizing includes generating sub-goals based on the specification. Each of the synthesized programs include a solved subset of sub-goals and each sub-goal includes a symbol in the DSL. The symbol is transformed based on the DSL. The sub-goals are solved based on the transforming of the symbol using the input and the corresponding prefix of the output to generate the synthesized programs. The prefix of the output matches a prefix of an output from each of the plurality of synthesized programs.

Systems, methods, and computer-executable instructions for synthesizing programs using a prefix of an output. A specification of a task to synthesize a program in a domain specific language (DSL) is received. The specification includes an input and a corresponding prefix of the output. Programs for the task are synthesized. The synthesizing includes generating sub-goals based on the specification. Each of the synthesized programs include a solved subset of sub-goals and each sub-goal includes a symbol in the DSL. The symbol is transformed based on the DSL. The sub-goals are solved based on the transforming of the symbol using the input and the corresponding prefix of the output to generate the synthesized programs. The prefix of the output matches a prefix of an output from each of the plurality of synthesized programs.

In an approach to AI explaining for natural language processing, responsive to receiving an input text for a machine learning model, an output is generated from the machine learning model. A plurality of alteration techniques are applied to the input text to generate one or more alternate outputs, where each alternate output corresponds to an alteration technique. A variation rate of the alternate output is calculated for each alteration technique. A preferred technique of generating neighboring data of the input text is generated based on a comparison of the variation rate of the alternate output for each alteration technique.