Microsoft Technology Licensing, LLC

Daniel Fernando Campos

Chenyan Xiong

Paul Nathan Bennett

Junaid Ahmed

Arnold Overwijk

Saurabh Kumar Tiwary

Li Xiong

Xia Song

Chuan Hu

Corby Louis Rosset

Nicholas Eric Craswell

Nicholas Craswell

A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Using a multi-task-trained neural network to guide interaction with a query-processing system via useful suggestions

A system for extracting a key phrase from a document includes a neural key phrase extraction model ('BLING-KPE') having a first layer to extract a word sequence from the document, a second layer to represent each word in the word sequence by ELMo embedding, position embedding, and visual features, and a third layer to concatenate the ELMo embedding, the position embedding, and the visual features to produce hybrid word embeddings. A convolutional transformer models the hybrid word embeddings to n-gram embeddings, and a feedforward layer converts the n-gram embeddings into a probability distribution over a set of n-grams and calculates a key phrase score of each n-gram. The neural key phrase extraction model is trained on annotated data based on a labeled loss function to compute cross entropy loss of the key phrase score of each n-gram as compared with a label from the annotated dataset.

Keyphase extraction beyond language modeling

Keyphrase extraction beyond language modeling

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Daniel Fernando Campos