<-AI Modules Go to ToC Datasets->
1. Technical Specification | 2. Conformance Testing | 3. Performance Assessment |
1. Technical Specifications
This page gives the links to the specification of Data Types specified by Technical Specification: Multimodal Conversation (MPAI-MMC) V2.4. All previously specified MPAI-MMC Data Types are superseded by those specified by V2.4.
Acronym | Name | JSON | Acronym | Name | JSON |
MMC-ECS | Cognitive State | X | MMC-SPD | Speech Descriptors | X |
MMC-EEM | Emotion | X | MMC-SOV | Speech Overlap | X |
MMC-FPS | Face Personal Status | X | MMC-SPS | Speech Personal Status | X |
MMC-GPS | Gesture Personal Status | X | MMC-SUM | Summary | X |
MMC-INT | Intention | X | MMC-TXD | Text Descriptors | X |
MMC-MEA | Meaning | X | MMC-TPS | Text Personal Status | X |
MMC-EPS | Personal Status | X | MMC-TXS | Text Segment | X |
MMC-ESC | Social Attitude | X | MMC-TXW | Text Word | X |
2 Conformance testing
A Data instance of a Data Type specified by MPAI-MMC V2.4 Conforms with it if the JSON Data validate against the relevant MPAI-MMC V2.4 JSON Schema and if the Data Conforms with the relevant Data Qualifier, if present. MPAI-MMC V2.4 does not provide method for testing the Conformance of the Semantics of the Data instance to the MPAI-MMC V2.4 specification.
Conformance testing can be performed by a human using a JSON Validator to verify the Conformance of the syntax of JSON Data to the relevant JSON Schema; and, if the Data has a Qualifier, to verify that the syntax of the Data conforms with the relevant values in the Data Qualifier. Alternatively, Conformance testing can be performed by software implementing the steps above.
3 Performance Assessment
Performance is a multidimensional entity because it can have various connotations, and the Performance Assessment Specification should provide methods to measure how well an AIW performs its function, using a metric that depends on the nature of the function, such as:
- Quality: Performance Assessment measures the quality of the Data instance using a metric that depends on the nature of the Data, e.g., the word error rate (WER) of a string of characters representing a sentence compared to an idea sentence.
- Bias: Performance Assessment uses a metric that depends on the bias in the Data compared with reference Data related to certain attributes of the Data. For instance, the Data may contain information about a particular geographic area when the ideal data do not .
- Legal compliance: Performance Assessment uses an appropriate metric to measure how well the Data instance complies with with a certain legal standard.