Microsoft Technology Licensing, LLC

Miaosen Wang

Ahmed Hassan Awadallah

Wei W Wang

Ryen William White

Xiao Yang

Madian Khabsa

Yu Su

Representative embodiments disclose mechanisms to map natural language input to an application programming interface (API) call. The natural language input is first mapped to an API frame, which is a representation of the API call without any API call formatting. The mapping from natural language input to API frame is performed using a trained sequence to sequence neural model. The sequence to sequence neural model is decomposed into small prediction units called modules. Each module is highly specialized at predicting a pre-defined kind of sequence output. The output of the modules can be displayed in an interactive user interface that allows the user to add, remove, and/or modify the output of the individual modules. The user input can be used as further training data. The API frame is mapped to an API call using a deterministic mapping.

Natural language to API conversion

A method for applying a trained machine learning model to answer a user query comprises receiving a query text from a user. A previously-trained discriminator is received, the previously-trained discriminator configured to output, for a pair of sentences, a match value indicating a quality of semantic match between the pair of sentences. For each candidate answer text of a plurality of candidate answer texts, the previously-trained discriminator is operated to output a candidate match value for the query text and the candidate answer text based on comparing a first hierarchy of representations of the query text at increasing degrees of semantic compression to a second hierarchy of representations of the candidate answer text at increasing degrees of semantic compression. An answer text is output that is associated with a highest candidate match value among candidate match values.

Multi-scale model for semantic matching

A method for estimating a quality of semantic match of a first sentence to a second sentence comprises outputting a first hierarchy of representations of the first sentence at increasing degrees of semantic compression, outputting a second hierarchy of representations of the second sentence at increasing degrees of semantic compression, comparing a selected representation in the first hierarchy to each of a plurality of representations in the second hierarchy, comparing a selected representation in the second hierarchy to each of a plurality of representations in the first hierarchy, and outputting a match value indicating a quality of semantic match between the first sentence and the second sentence, the match value based at least on the comparisons.

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Miaosen Wang