Robert Boschgmbh

Facebook, Inc.

Bingqing Wang

Zhe Feng

Tianshi Gao

Pengjun Pei

Anna C Hätty

Kaiqiang Song

Ishan Dindorkar

Lars Franke

Disclosed are systems and methods for a computerized framework that provides a document structure parsing system for requirement engineering documents, where the logical structure of the text is not available, and is to be rebuilt based on the raw textual content. The framework approaches the build of the logical structure according to two phases. The first phase involves creating a list of list of text snippets from the raw text, where sequence labeling is adopted to re-segment and merge initially segmented text snippets. The second phase involves the framework executing computerized techniques including embedding adaptation approach, a hierarchy structure rebuilt algorithm, and a requirement text selection strategy to rebuild the hierarchy structure.

Computerized systems and methods for hierarchical structure parsing and building

A computer-implemented system and method relate to natural language processing. A candidate is selected based on a query. The candidate includes a question and an answer to that question. The candidate is classified as belonging to a class. The class indicates that the candidate is relevant to the query. The class is selected from among a group of classes, which includes at least that class and another class. The another class indicates that the candidate has the answer to the query. Upon classifying the candidate as belonging to the class, a machine learning system is configured to generate a relevant response for the query upon receiving the query and the question as input data. The relevant response provides information associated with a keyword of the question that is not contained in the query.

Retrieval-based dialogue system with relevant responses

A generative automatic text summarization system and method is disclosed that may adopt a search and reranking strategy to improve the performance of a summarization task. The system and method may employ a transformer neural model to assist with the summarization task. The transformer neural model may be trained to learn human abstracts and may then be operable to generate abstractive summaries. With multiple summary hypothesis generated, a best-first search algorithm and reranking algorithm may be employed to select the best candidate summary as part of the output summary.

Generative text summarization system and method

A method of determining an operational difficulty of a process described in a document including text includes generating text data corresponding to the text of the document, and generating a word vector output by processing the text data through a plurality of word vectors, the plurality of word vectors based on a domain-specific text collection and a general-language text collection. The method further includes generating a statistical information output by processing the text data through a plurality of statistical information vectors, the plurality of statistical information vectors based on the domain-specific text collection and the general-language text collection, and generating a gated recurring unit (GRU) output by processing (i) the word vector output, (ii) the statistical information output, (iii) word-level data based on the text data, (iv) sentence-level data based on the text data, and (v) paragraph-level data based on the text data through at least one GRU.

Domain-specific process difficulty prediction

To present one or more content to users of an online system, the online system identifies a content evaluation pipeline including an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages in the order. The online system identifies a set of candidate modifications to one or more operational parameters of the content evaluation pipeline. For each candidate modification, the online system determines a compute time value and a content selection value. For a given amount of compute time, the online system optimizes the one or more operational parameters based on the determined content time value and the determined content selection value to increase the content selection value of the content evaluation pipeline.

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Mountain View, CA, United States of America

Bingqing Wang