For example, a "result" can contain multiple "interpretations", each of which is taken to be an alternative. The element "xf:model" is an XForms data model as specified in the XForms data model draft, and therefore is not defined in this document. To illustrate the basic usage of these elements, as a simple example, consider the utterance ok. This example includes only the minimum required information, i. There is an overall "result" element which includes one interpretation. This external model defines a "response" element. The "myApp" namespace refers to the application-specific elements that are defined by the XForms data model.
The root element of the markup is "result". The "result" element includes one or more " interpretation " elements. Multiple interpretations result from ambiguities in the input or in the semantic interpretation. If the "grammar", "x-model", and "xmlns" attributes don't apply to all of the interpretations in the result they can be overridden for individual interpretations at the "interpretation" level.
Interpretations must be sorted best-first by some measure of "goodness". The goodness measure is "confidence" if present, otherwise, it is some platform-specific indication of quality. The x-model and grammar are expected to be specified most frequently at the "result" level, because most often one data model will be sufficient for the entire result. However, it can be overridden at the "interpretation" level because it is possible that different interpretations may have different data models - perhaps because they match different grammar rules. The "interpretation" element includes an " input " element which contains the input being analyzed, optionally a " model " element defining the XForms data model and an " instance " element containing the instantiation of the data model for this utterance.
The data model would be empty if the interpreter was not able to produce any interpretation. The "model" element contains an XForms data model for the data and is part of the X-Forms name space.
The XForms data model provides for a structured data model consisting of groups, which may contain other groups or simple types. Simple types can be one of: string, boolean, number, monetary values, date, time of day, duration, URI, binary. For further information on XForms data models see the X-Forms data model specification. Note that XForms fields default to optional. If no data model is supplied by either the "model" element or the "x-model" attribute then it is assumed that the data model will be provided by the dialog or whatever other process receives the NL semantic mark-up.
The "instance" element contains an instance of the XForms data model for the data and is part of the XForms name space. The use of a confidence attribute from the NL semantics namespace does not appear to present any document validation problems. However if future XForms specifications support an equivalent attribute then that would be preferable to the current proposal. The "input" element is the text representation of a user's input. It includes an optional "confidence" attribute which indicates the recognizer's confidence in the recognition result not the confidence in the interpretation, which is indicated by the "confidence" attribute of "interpretation".
Note that it doesn't make sense for temporally overlapping inputs to have the same mode; however, this constraint is not expected to be enforced by platforms. Having additional input elements allows the representation to support future multi-modal inputs as well as finer-grained speech information, such as timestamps for individual words and word-level confidences. The "nomatch" element under "input" is used to indicate that the natural language interpreter was unable to successfully match any input.
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It can optionally contain the text of the best of the rejected matches. The "noinput" element under "input" is used to indicate that there was no input-- a timeout occurred in the speech recognizer due to silence. If there are multiple levels of inputs, it appears that the most natural place for the "nomatch" and "noinput" elements is under the highest level of "input" for "no input", and under the appropriate level of "input" for "nomatch".
So "noinput" means "no input at all" and "nomatch" means "no match in speech modality" or "no match in dtmf modality". For example, to represent garbled speech combined with dtmf "1 2 3 4", we would have the following:. The natural language requirements state that the semantics specification must be capable of representing a number of types of meta-dialog and meta-task utterances.
This specification is flexible enough so that meta utterances can be represented on an application-specific basis without defining specific formats in this specification. This specification can be used on an application-specific basis to represent utterances that contain unresolved anaphoric and deictic references. Anaphoric references, which include pronouns and definite noun phrases that refer to something that was mentioned in the preceding linguistic context, and deictic references, which refer to something that is present in the non-linguistic context, present similar problems in that there may not be sufficient unambiguous linguistic context to determine what their exact place in the data instance should be.
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In order to represent unresolved anaphora and deixis using this specification, the developer must define a more surface-oriented representation that leaves the interpretation of the reference open. This assumes that a later component is responsible for actually resolving the reference. One of the natural language requirements states that the specification must be extensible. The specification supports this requirement because of its flexibility, as discussed in the discussions of meta utterances and anaphora.
The markup can easily be used in sophisticated systems to convey application-specific information that more basic systems would not make use of, for example defining speech acts, if this is meaningful to the dialog manager. Defining standard representations for items such as dates, times, etc. Leading and trailing spaces in utterances are not significant. This representation assumes there are no ambiguities in the speech or natural language processing. Note that this representation also assumes some level of intrasentential anaphora resolution, i. A combination of dtmf input and speech would be represented using nested input elements.
For example:. In this mark-up ambiguities are only represented at the top-level, using separate interpretation elements. Representation of "local" ambiguities, for example, at the level of an ambiguity between two ingredients peppers vs. The more compact representation using local ambiguities has not been defined for three reasons:. Local ambiguities may be supported in the future if representation of ambiguity becomes part of the XForms standard.
Natural language ambiguities result from syntactic, semantic, or pragmatic ambiguities in a single recognizer result. For example in I want fried onions and peppers, there are two interpretations, one in which the peppers are to be fried and one in which they are not to be fried. This attribute would not be meaningful if there is only one interpretation. This information could be used, for example, by a dialog manager to construct a more helpful response e. I didn't hear that vs. I didn't understand that or by a scoring algorithm that treats different ambiguity sources differently.
In many cases identical information can be conveyed in one utterance or over the course of several dialog turns. This situation can occur both in the case of a subdialog or in the case of a reusable component. For example, if the system's goal in the subdialog or the reusable component is to collect travel information from a user, the ultimate information is the same whether the user says I want to go from Pittsburgh to Seattle on January 1, , in a single utterance or whether the same information is elicited from the user during several dialog turns, as in.
System: Where will you be departing from?
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User: Pittsburgh. System: Where will you be traveling to? User: Seattle. It should be possible to use a substantially similar semantic representation in both of these situations. The main issue is that in the case of information collected over the course of a dialog it becomes very difficult to tie that information back to the original inputs. Elements such as "input" and attributes such as "timestamp-start", "timestamp-end", "grammar", and "mode" which relate the semantic interpretation directly to the input become less meaningful when the information is collected in a dialog.
Moreover, they also become less useful to the main dialog component, since presumably it's the function of the subdialog or reusable component to make use of this low-level information internally to guide its own dialog and to shield the main dialog from these details. Toggle navigation Additional Book Information.
Description Table of Contents Reviews. Summary Recognizing that the generation of natural language is a goal- driven process, where many of the goals are pragmatic i. Each chapter states a problem that arises in generation, develops a pragmatics-based solution, and then describes how the solution is implemented in PAULINE, a language generator that can produce numerous versions of a single underlying message, depending on its setting. Table of Contents Contents: Introduction. Interpretation in Generation. Affect in Text. Creating Style. Grammar and A Phrasal Lexicon. Planning and Realization.
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