Alexander Franz

Bio: Alexander Franz is an academic researcher from Google. The author has contributed to research in topics: Language model & Machine translation. The author has an hindex of 24, co-authored 45 publications receiving 2984 citations. Previous affiliations of Alexander Franz include Caterpillar Inc. & Sony Broadcast & Professional Research Laboratories.

Integrated authoring and translation system

Abstract: The present invention is a system of integrated, computer-based processes for monolingual information development and multilingual translation. An interactive text editor enforces lexical and grammatical constraints on a natural language subset used by the authors to create their text, which they help disambiguate to ensure translatability. The resulting translatable source language text undergoes machine translation into any one of a set of target languages, without the translated text requiring any postediting.

A method and apparatus for interactive source language expression recognition and alternative hypothesis presentation and selection

Abstract: In a language translation system, a method for providing a guide for a user. In one embodiment, the method comprises the steps of receiving (302) an input that is representative of at least one word in a source language, and generating (1402) at least one recognition hypothesis in the source language in response to the input. The method further comprises the steps of selecting (406) a best hypothesis from the at least one recognition hypothesis in the source language, presenting (408) the best hypothesis in the source language to a user, and presenting (408) alternatives to a portion of the best hypothesis in the source language to the user. The method further includes receiving (410) an indication of a choice of one of the alternatives from the user, and presenting (306) a revised version of the best hypothesis including the alternative chosen to the user.

Voice interface for a search engine

Abstract: A system provides search results from a voice search query. The system receives a voice search query from a user, derives one or more recognition hypotheses, each being associated with a weight, from the voice search query, and constructs a weighted boolean query using the recognition hypotheses. The system then provides the weighted boolean query to a search system and provides the results of the search system to a user.

A method and apparatus for adaptive speech recognition hypothesis construction and selection in a spoken language translation system

Abstract: A method and apparatus for adaptive speech recognition hypothesis are provided, wherein a number of ordered recognition hypotheses are generated and presented in response to a received speech input comprising natural spoken language. Generation of the recognition hypotheses comprises assigning (408) basic probabilities to at least one basic component of the speech input using language models and calculating (410) an overall probability of each of the recognition hypotheses using the assigned basic probabilities. The best hypothesis is selected (1802) by a user from the recognition hypotheses. Hypothesis generation is adapted (1810) in response to the selected best hypotheses, wherein the selected hypothesis is analyzed, a list comprising the basic components of the selected best hypothesis and the assigned basic probabilities is generated, credit is assigned to the basic components of the selected hypothesis by raising the assigned basic probabilities, and the basic probabilities of the language model are renormalized. An output is provided (306) comprising the best hypothesis; moreover, the input is translated in response to the selected best hypothesis, and a synthesized translated speech output is provided.

Identifying language attributes through probabilistic analysis

Abstract: A system and method for identifying language attributes through probabilistic analysis is described. A set of language classes and a plurality of training documents are defined, Each language class identifies a language and a character set encoding. Occurrences of one or more document properties within each training document are evaluated. For each language class, a probability for the document properties set conditioned on the occurrence of the language class is calculated. Byte occurrences within each training document are evaluated. For each language class, a probability for the byte occurrences conditioned on the occurrence of the language class is calculated.

Intelligent Automated Assistant

Abstract: An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Systems and methods for responding to natural language speech utterance

Abstract: Systems and methods are provided for receiving speech and non-speech communications of natural language questions and/or commands, transcribing the speech and non-speech communications to textual messages, and executing the questions and/or commands. The invention applies context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for one or more users presenting questions or commands across multiple domains. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech and non-speech communications and presenting the expected results for a particular question or command.

A Syntax-based Statistical Translation Model

Abstract: We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.

Mobile systems and methods of supporting natural language human-machine interactions

Abstract: A mobile system is provided that includes speech-based and non-speech-based interfaces for telematics applications. The mobile system identifies and uses context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for users that submit requests and/or commands in multiple domains. The invention creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context and presenting the expected results for a particular question or command. The invention may organize domain specific behavior and information into agents, that are distributable or updateable over a wide area network.

Contextual Mobile Content Placement on a Mobile Communication Facility

Abstract: In embodiments of the present invention improved capabilities are described for displaying mobile content in association with a website on a mobile communication facility based at least in part on receiving a website request from a mobile carrier gateway, receiving contextual information relating to the requested website, associating the received contextual information with a mobile content, and, finally, displaying the mobile content with the website on a mobile communication facility.