Nuance Communications, Inc.

Ding Liu

William F Ganong, Iii

Paul Joseph Vozila

Peter R Stubley

Real Tremblay

Serge Robillard

Jerome Tremblay

Jean-Francois Beaumont

A method, computer program product, and computer system for identifying, by a computing device, a model for predicting conversational phrases for a communication between at least a first user and a second user. The model may be trained based upon, at least in part, an attribute associated with the second user. At least one conversational phrase may be predicted for the communication between the first user and the second user. The at least one conversational phrase may be provided to the second user as an optional phrase to be sent to the first user.

System and method for accelerating user agent chats

Typical frequently-asked question (FAQ) systems perform keyword searches on queries to a database of answers to deliver an answer to a user. The present invention searches a user generated query in a classifier trained to search a database of questions in a set of FAQs. The questions in the database correspond to answers. Upon finding a relevant question, the system returns the corresponding answer to the user. Searching the query against the questions generates more accurate results for the user.

Method and apparatus for operating a frequently asked questions (FAQ)-based system

In FAQ based systems, associating questions with answers can be a time consuming task if performed manually. In one embodiment, a method of building a frequently-asked questions (FAQ) portal can include creating cluster labels. The labels can include predefined universal semantic labels and application-specific labels. The method can further include applying the cluster labels to clusters of queries within an FAQ application. The method can additionally include adjusting the application-specific labels to support combined and newly created clusters of queries based on application-specific queries within the FAQ application on an ongoing basis and reapplying the universal semantic labels and the adjusted application-specific labels to the combined and newly created clusters of queries. The method and system proposed herein allow for the automated clustering of queries and association with applicable answers, which leads to higher efficiencies for a faster response time for a user.

Method and apparatus for a frequently-asked questions portal workflow

A frequently-asked-question (FAQ)-based system receives question(s) from a user and generates answer(s) based on data about the question(s). In one embodiment, a method includes retrieving, from a memory, a global structure and candidate answers therein. The method can include computing a first, second, and third probability of a candidate answer based on a local structure of the candidate answer within the global structure, content of the candidate answer given content of a query and context of the candidate answer given the content of the query, respectively. The method can include providing a combined probability of the candidate answer based on the first probability, second probability, and third probability. The method can improve efficiency of a FAQ-based system by automating organization of semi-structured data in a database. Therefore, a human user does not need to manually generate the database when it is already generated in semi-structured form, a semi-structured HTML document.

System and method for learning answers to frequently asked questions from a semi-structured data source

According to example configurations, a speech-processing system parses an uttered sentence into segments. The speech-processing system translates each of the segments in the uttered sentence into candidate textual expressions (i.e., phrases of one or more words) in a first language. The uttered sentence can include multiple phrases or candidate textual expressions. Additionally, the speech-processing system translates each of the candidate textual expressions into candidate textual phrases in a second language. Based at least in part on a product of confidence values associated with the candidate textual expressions in the first language and confidence values associated with the candidate textual phrases in the second language, the speech-processing system produces a confidence metric for each of the candidate textual phrases in the second language. The confidence metric can indicate degree to which the candidate textual phrase in the second language is an accurate translation of a respective segment in the utterance.

Speech and language translation of an utterance

Techniques disclosed herein include systems and methods for creating a bootstrap call router for other languages by using selected N-best translations. Techniques include using N-best translations from a machine translation system so as to increase a possibility that desired keywords in a target language are covered in the machine translation output. A 1-best translation is added to a new text corpus. This is followed by selecting a subset that provides a varied set of translations for a given source transcribed utterance for better translation coverage. Additional translations are added to the new text corpus based on a measure of possible translations having words not yet seen for the selected transcribed utterances, and also based on possible translation having words that are not associated with very many or semantic tags in the new text corpus. Candidate translations can be selected from a window of N-best translations calculated based on machine translation accuracy.

Bootstrap call router to other languages using selected N-best translations

A method is described for semantic classification in human-machine dialog applications, for example, call routing. Utterances in a new training corpus of a new semantic classification application are tagged using a pre-existing semantic classifier and associated pre-existing classification tags trained for an earlier semantic classification application.

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Lowell, MA, United States of America

Ding Liu