Abstract: Techniques are disclosed herein for integrating document question answering in an artificial intelligence-based platform, such as a chatbot system. The techniques include receiving a query from a user, rewriting the query to include one or more specific descriptors, computing an embedding vector for the rewritten query, retrieving one or more textual passages from a document store utilizing the embedding vector for the rewritten query, determining one or more answers to the rewritten query within the one or more textual passages, and returning the one or more answers.

Abstract: Techniques for maintaining list-type text formatting when converting content from a source content format to a destination content format are disclosed. A system generates text content by applying text formatting tags to segments of characters obtained from a source electronic document. The system parses a static-display type source electronic document to obtain character data of the characters in the source document. The system analyzes the parsed data to identify text arranged in a list-type text format in the source document. The system generates text content in a destination content format different from the source format by applying tags to segments of the text content designating the segments items in a list.

Abstract: Techniques for generating text content arranged in a consistent read order from a source document including text corresponding to different read orders are disclosed. A system parses a binary file representing an electronic document to identify characters and metadata associated with the characters. The system pre-sorts a character order of characters in each line of the electronic document to generate an ordered list of characters arranged according to the right-to-left reading order. The system performs a layout-mirroring operation to change a position of characters within the modified document relative to a right edge of the document and a left edge of the document. Subsequent to performing layout-mirroring, the system identifies native left-to-right reading-order text in-line with the native right-to-left reading-order text.

Abstract: Techniques for generating formatting tags for textual content obtained from a source electronic document are disclosed. A system parses a digital file to obtain information about characters in an electronic document. The system applies tags to text generated based on the textual content of the electronic document by creating segments of textually-consecutive characters and applying corresponding text formatting style tags to the segments. The system further identifies segments of text overlapping bounding boxes in the electronic document. The system generates textual content including a segment of text and a corresponding hyperlink associated with the segment of text. The system further generates textual content by selectively applying line breaks from the source electronic document in the textual content.

Abstract: Techniques for generating text content arranged in a consistent read order from a source document including text corresponding to different read orders are disclosed. A system parses a binary file representing an electronic document to identify characters and metadata associated with the characters. The system pre-sorts a character order of characters in each line of the electronic document to generate an ordered list of characters arranged according to the right-to-left reading order. The system performs a layout-mirroring operation to change a position of characters within the modified document relative to a right edge of the document and a left edge of the document. Subsequent to performing layout-mirroring, the system identifies native left-to-right reading-order text in-line with the native right-to-left reading-order text.

Abstract: The present disclosure relates to a video-based interaction method and apparatus, storage medium, and electronic device. The video-based interaction method comprises: when a target video is played to a target image frame, pausing playback of the target video, and displaying an obscuration comprising an interaction control on a current playback interface corresponding to the target image frame, wherein the interaction control is configured to perform a control operation on a target display object in the target image frame; and in response to a triggering operation of a user on the interaction control, displaying an interactive page comprising the target display object, and applying the control operation on the target display object on the interactive page.

Abstract: Provided are an interaction method and apparatus, an electronic device, and a storage medium. The method includes displaying a target page, where target content containing a target object is displayed in the target page; receiving a shake trigger operation of a user; and in response to the shake trigger operation, displaying associated content of the target content in a layer above the target page and displaying the detail page of the target object after completing displaying the associated content.

Abstract: Techniques disclosed herein relate generally to language detection. In one particular aspect, a method is provided that includes obtaining a sequence of n-grams of a textual unit; using an embedding layer to obtain an ordered plurality of embedding vectors for the sequence of n-grams; using a deep network to obtain an encoded vector that is based on the ordered plurality of embedding vectors; and using a classifier to obtain a language prediction for the textual unit that is based on the encoded vector. The deep network includes an attention mechanism, and using the embedding layer to obtain the ordered plurality of embedding vectors comprises, for each n-gram in the sequence of n-grams: obtaining hash values for the n-gram; based on the hash values, selecting component vectors from among the plurality of component vectors; and obtaining an embedding vector for the n-gram that is based on the component vectors.

Abstract: Deep learning techniques are disclosed for extraction of embedded data from documents. In an exemplary technique, a set of unstructured text data is received. One or more text groupings are generated by processing the set of unstructured text data. One or more text grouping embeddings are generated in a format for input to a machine learning model based on the one or more generated text groupings. One or more output predictions are generated by inputting the one or more text grouping embeddings into the machine learning model. Each output prediction of the one or more output predictions correspond to a predicted aspect of a text grouping of the one or more text groupings.

Abstract: Techniques are disclosed for rules-based techniques for extraction of question-and-answer pairs from digital documents. In an exemplary technique, a digital text document can be accessed by executing a document indicator. A document hierarchy can be generated. The document hierarchy can include at least one parent node and child nodes corresponding to the parent node. Each node of the child nodes can correspond to a text feature of the digital text document. At least a first child node of the child nodes can be determined that corresponds to a first text feature. The parent node of the first child node and a second child node of the parent node can be determined. The second child node can correspond to a second text feature related to the first text feature. A training data set can be generated.

Abstract: Techniques for extracting key information from a document using machine-learning models in a chatbot system is disclosed herein. In one particular aspect, a method is provided that includes receiving a set of data, which includes key fields, within a document at a data processing system that includes a table detection module, a key information extraction module, and a table extraction module. Text information and corresponding location data are extracted via optical character recognition. The table detection module detects whether one or more tables are present in the document and, if applicable, a location of each of the tables. The key information extraction module extracts text from the key fields. The table extraction module extracts each of the tables based on input from the optical character recognition and the table detection module. Extraction results include the text from the key fields and each of the tables can be output.