Patent Application
20150032441
This Application is related to “Method and Apparatus for Selecting Among Competing Models in a Tool for Building Natural Language Understanding Models” by Jeffrey N. Marcus, attorney docket number 4765.1034-000, filed on the same day as this Application to a common assignee. The foregoing application is hereby incorporated by reference in its entirety.
When a designer wants to build a Natural Language Understanding (NLU) model for a new application (e.g., an airline reservation system), the designer starts a new project and configures that project with an ontology. The ontology is an an organizing structure of concepts that the NLU application handles. For example, a project may handle fields such as DATE, LOCATION, DATERANGE, or AMOUNT, etc. Typically, NLU models for many of these concepts are built-in within a designer portal and other more specialized NLU models (such as the airplane seating types for a particular airline) may need to be created by the designer.
Natural Language Understanding (NLU) applications often include many concepts. A non-expert may experience difficulty identifying built-ins (pre-made fields) to use in an NLU project. Online documentation can assist the designer; however, a more intuitive user interface would allow the designer to type in examples of expected sentences for the NLU application. For those examples, the designer can use an embodiment of the present invention to pre-populate the project with the matching built-ins. Additionally, the embodiment of the present invention identifies sentences that do not match any concepts well for which the designer needs to create the concepts. Since NLU application designers often start off by writing “sample dialogs” consisting of system-generated prompts followed by designer responses, discover-by-example is a natural way to configure a project.
Discover-content-by-example includes a set of natural language (NL) models for interpreting concepts such as DATE, DATERANGE, AMOUNT, etc. The designer types in expected sentences from users using the NLU application to say within the dialog, perhaps with some context such as the general business type (travel, banking, etc.). The phrase is processed by all NL models included in a portal, and then each model returns a semantic confidence score, which can be arbitrarily defined. Based on a designer-defined threshold, zero or more models are identified as matching the sentence. If exactly one model is identified, it can be loaded into the project ontology. If more than one model is identified, the designer can choose one or more models from among them. If no models are identified, the system can display a message that no good match has been found, and the designer can create a new model.
Further, the system can be extended to discover contents by sample system prompts (e.g., the prompts the system plays to the user to elicit his responses) instead of sample user responses. This may be even more powerful since the designer has control over prompts but has to guess at user responses. A database may be built from previously created or previously released deployments consisting of prompt text and associated responses by the user, with each user response precompiled into the concepts used to form it. When the designer types in a sample prompt from the corresponding application, the sample prompt is run against the database, and a “prompt match score” is assigned for each prompt in the database using some measure of semantic similarity. If there is a high match score, the set of concepts that are associated with that prompt are loaded into the NL Portal project.
For example, a sample prompt may be “When would you like to travel?”. The database may have a prompt of “When are you travelling?” associated with the concepts DATE and TIME. This sentence is given a large semantic match score with the sentence in the database. A semantic match score is a score indicating the relatedness of two sentences. A high score indicates that two sentences are highly related and a low score indicates that the two sentences are not highly related. DATE and TIME is automatically loaded into the project ontology because of the semantic match score of the two sentences and the association with the database's prompt with the DATE and TIME concepts.
In one embodiment, a method for initializing a workspace for building a Natural Language Understanding (NLU) system includes parsing a sample sentence to select at least one candidate stub grammar from among multiple candidate stub grammars. The term “stub grammar” is used herein, and in any continuing applications, to refer to a subset of a grammar, where a “grammar” is understood in the art to mean a model that correlates an input of a user to a response to the user.
The method can further include presenting respective representations of the candidate stub grammars selected by the parsing of the sample sentence to a user. The method can additionally include enabling the user to choose one of the respective representation of the candidate stub grammars. The method can further include adding to the workspace a stub grammar corresponding to the representation of the candidate stub grammar chosen by the user.
The respective representations can be names or identifiers of the candidate stub grammar or contents of the candidate stub grammar itself.
If the prasing of the sample sentence selects one candidate stub grammar from among multiple candidate stub grammars, the method can skip presenting the a respective representation of the one candidate stub grammar to the user and also skip enabling the user to choose the one candidate stub grammar. The method can include directly adding the one candidate stub grammar to the workplace (e.g., without user input since there is only grammar to choose from).
In another embodiment, the method can include presenting each respective representation of the candidate stub grammars to the user by visually presenting each of the respective representations of the candidate stub grammars relative to a corresponding part of the sample sentence.
In yet a further embodiment, parsing the sample sentence includes parsing the sample sentence to generate a set of annotations including mentions. The annotations can further include mentions and intents. An intent refers to an end user's basic goal for a machine interaction. A mention refers to a piece of information the natural language application uses to complete the action indicated by the intent.
In another embodiment, the presented respective representations can have a semantic match to the parsed sample sentence above a particular threshold.
In another embodiment, a system for initializing a workspace for building a Natural Language Understanding (NLU) system can include a parsing module configured to parse a sample sentence to select at least one candidate stub grammar from among multiple candidate stub grammars. The system can further include a presentation module configured to present respective representations of the candidate stub grammars selected by the parsing of the sample sentence to the user. The system can further include a user interface module configured to enable the user to choose one of the respective representations of the candidate stub grammars. The system can additionally include a workspace building module configured to add a chosen grammar, chosen by the user from among the respective presented representations, to the workspace.
In another embodiment, a non-transitory computer-readable medium can be configured to store instructions for initializing a workspace for building a Natural Language Understanding (NLU) system. The instructions, when loaded and executed by a processor, can cause the processor to parse a sample sentence to select at least one candidate stub grammar from among multiple candidate stub grammars. The instructions can further cause the processor to present respective representations of the at least one candidate stub grammar selected by the parsing of the sample sentence to a user. The instructions can additionally cause the processor to enable the user to choose one of the respective representation of the candidate stub grammars. The instructions can further cause the processor to add to the workspace a stub grammar corresponding to the representation of the candidate stub grammar chosen by the user.
The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention.
A description of example embodiments of the invention follows.
The project 112 can be a workplace or model that is to be used for building a natural language understanding (NLU) system. If the portal module 106 finds only one stub grammar with a high semantic match to the sample sentence, the portal module 106 can forward the stub grammar 108 directly to the project 112 without asking the user 103 for input because the user 103 has no choice of which stub grammar to select. However, the portal module 106 can be configured to present the user with the choice of selecting the one stub grammar 108 or selecting no grammar at all
On the other hand, if the system does not determine that the candidate stub grammar has a high semantic match to the sample sentence, the system determines whether there are more candidate stub grammars to be matched (312). If so, the system loads the next candidate stub grammar (314), and if not, the system presents a representation of the candidate stub grammars in the buffer to the user for selection (316). The representation of the candidate stub grammars can be a name of the grammar, a description of the grammar, representation of fields of the stub grammar, or other representation of the grammar.
For example, the user interface 420 presents a field 410a of the first stub grammar 408 of “destination” associated with a first parsed word 406a (e.g., “restaurants”) and a field 410b of the second stub grammar 408b that is blank associated with the first parsed word 406a. The user interface presents respective fields 410c-d of the first and second stub grammars 408a-bassociated with the second parsed word 406b of “8PM,” each field 410c-d having the value of “time.” The user interface 420 also presents respective fields 410e-f of the first and second stub grammars 408a-bhaving the value of “place” associated with a third parsed word 406c of “Boston.”
A display or other device, via the user interface 420, presents the user with the choices of the first stub grammar 408a and second stub grammar 408b. The user can select, for instance using a mouse, touch screen, or other selection device, a stub grammar to use to begin the NLU project, workspace, or model. For example, the user can select the first stub grammar 408a because the fields 410a, 410c, and 410e are close matches to the parsed words 406a-c.
Client computer(s)/devices 50 and server computer(s) 60 provide processing, storage, and input/output devices executing application programs and the like. The client computer(s)/devices 50 can also be linked through communications network 70 to other computing devices, including other client devices/processes 50 and server computer(s) 60. The communications network 70 can be part of a remote access network, a global network (e.g., the Internet), a worldwide collection of computers, local area or wide area networks, and gateways that currently use respective protocols (TCP/IP, Bluetooth®, etc.) to communicate with one another. Other electronic device/computer network architectures are suitable.
In one embodiment, the processor routines 92 and data 94 are a computer program product (generally referenced 92), including a non-transitory computer-readable medium (e.g., a removable storage medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. The computer program product 92 can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable communication and/or wireless connection. In other embodiments, the invention programs are a computer program propagated signal product embodied on a propagated signal on a propagation medium (e.g., a radio wave, an infrared wave, a laser wave, a sound wave, or an electrical wave propagated over a global network such as the Internet, or other network(s)). Such carrier medium or signals may be employed to provide at least a portion of the software instructions for the present invention routines/program 92.
In alternative embodiments, the propagated signal is an analog carrier wave or digital signal carried on the propagated medium. For example, the propagated signal may be a digitized signal propagated over a global network (e.g., the Internet), a telecommunications network, or other network. In one embodiment, the propagated signal is a signal that is transmitted over the propagation medium over a period of time, such as the instructions for a software application sent in packets over a network over a period of milliseconds, seconds, minutes, or longer.
While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.
