Microsoft Technology Licensing, LLC

Voicebox Technologies Corporation

Jiayuan Huang

Eslam K Abdelreheem

Changhong Yuan

Qiang Wu

Zheng Chen

Yi-Min Wang

Weizhu Chen

Pengcheng He

Ankit Saraf

Matthew Brigham Hall

Chao Li

Mingyu Wang

Jianwen Zhang

Yi Mao

Yu Zhao

Yuting Sun

Justin Carl Wong

Yuantao Wang

Jaclyn Ruth Elizabeth Phillips

Tsuyoshi Watanabe

Shean Wang

Yunjing Ma

Xiaoying Guo

Nan Zhao

Jade Huang

Vikas Bahirwani

Syed Muhammad Mujahid Hassan

Safiyyah Saleem

Yikun Guo

Yuangang Tu

Jiantao Sun

Shichao Hu

In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.

Iterative sampling based dataset clustering

Methods, systems, apparatuses, and computer program products are provided for extracting an entity value from a sentence. An embedding set that may include one or more sentence embeddings is generated for at least part of a first sentence that is tagged to associate a first named entity in the sentence with an entity type. A plurality of candidate embeddings is also generated for at least part of a second sentence. The one or more sentence embeddings in the embedding set may be compared with each of the plurality of candidate embeddings, and a match score may be assigned to each comparison to generate a match score set. A particular match score of the match score set may be identified that exceeds a similarity threshold, and an entity value of the entity type may be extracted from the second sentence associated with the identified match score.

Example based entity extraction, slot filling and value recommendation

Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution. In further examples, the operations include a dynamic application of a solution policy and a diagnosis policy in the multi-turn conversation, to determine whether to deliver a solution or ask diagnosis questions.

Knowledge-driven dialog support conversation system

The disclosure relates to methods, systems and other embodiments directed to determining an information domain match for a natural language (NL) input (e.g., a spoken utterance), and confirming whether the NL input is correctly matched to the information domain. For example, after receiving an NL input, a first information domain to which the NL input belongs and a feature value set may be determined based on a semantic pattern matching technique. Further, a second information domain to which the NL input belongs, and a corresponding confidence score related to the second information domain may be determined. The second information domain may be determined based on a first statistical classification technique. Based on the determined feature value set and the confidence score related to the second information domain, it may be confirmed whether the NL input correctly belongs to the first information domain, e.g., based on a second statistical classification technique.

System and method of determining a domain and/or an action related to a natural language input

An annotation system that provides annotations for a corresponding set of entities with regard to a user is presented. The annotation system annotates the entities independently of the selection of the entities to be annotated. Upon receiving a set of entities to annotate, the annotation system identifies a set of annotation relationships between the user and the entity. Each annotation relationship identifies a reason or basis for which the entity may be relevant to the user. The annotation relationships are scored to identify the strength of the relationship between the user and the entity. A configurable threshold number of annotation relationships with the greatest affinity between the user and the entity are selected as the annotation for the entity.

Annotating personalized recommendations

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Bellevue, WA, United States of America

Jiayuan Huang