Dialogue Annotation Ontology for Image Editing State Tracking
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Solution Overview
Problem
Conventional systems for annotating dialogue text in digital image editing are inaccurate, inefficient, and inflexible, particularly in handling multi-topic, highly-interactive, and incremental dialogues, and fail to accommodate open-ended instruction values common in image editing applications.
Innovation Solution
A digital image editing dialogue annotation system utilizing a frame-structure annotation ontology that manages both pre-defined and open-ended values, enabling co-reference resolution of objects and locations, and employing a trained classification algorithm to suggest annotation elements, thereby improving annotation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems use predefined dictionaries or knowledge bases for annotation, then annotation structure is standardized, but they cannot accommodate open-ended instruction values in image editing dialogue
Solution Approach 1:
The annotation system dynamically adapts its schema based on the specific dialogue context and instruction type. Rather than using a static predefined dictionary, the system allows the annotation structure to flexibly accommodate various open-ended values while maintaining standardized fields for consistent processing. This enables the system to handle diverse image editing instructions accurately.
Solution Approach 2:
The system changes the parameters of the annotation schema based on the input dialogue. Different instruction types (e.g., object manipulation, attribute modification, location-based edits) trigger different annotation configurations, allowing the system to optimize the annotation structure for each specific case while maintaining overall standardization.
2Adaptability or versatility
If conventional systems use a large number of surface-level annotation options to handle high-dimensional annotation applications, then they can accommodate larger variation in options, but they result in inefficiencies with regard to time and user interactions
Solution Approach 1:
The annotation system segments the annotation process into distinct phases: initial rapid annotation using simplified schemas, followed by iterative refinement. This segmentation allows annotators to quickly capture essential information without being overwhelmed by the full complexity of the annotation schema, thereby improving productivity while maintaining versatility.
Solution Approach 2:
The system performs preliminary annotation actions using automated suggestions and pre-filled fields based on dialogue analysis. This preliminary action reduces the manual effort required for complete annotation, allowing annotators to focus on refining and verifying rather than creating annotations from scratch, thus improving efficiency without sacrificing annotation quality.
3Device complexity
If conventional systems annotate dialogue text without frame structure, then annotation process is simpler, but they fail to capture the state-driven nature of incremental image editing dialogues
Solution Approach 1:
The annotation system implements a nested frame structure where dialogue annotations are organized hierarchically. Frames represent different levels of abstraction, with outer frames capturing overall dialogue state and inner frames capturing specific instruction details. This nesting allows the system to maintain detailed state information without overwhelming complexity in the annotation process.
Solution Approach 2:
The frame structure acts as an intermediary layer between the raw dialogue text and the final annotation. It mediates the transformation by structuring intermediate representations that preserve co-reference relationships and state information, making the annotation process more manageable while preventing information loss.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate ground truth annotations of target utterances in digital image editing dialogues in order to create a state-driven training data set. In particular, in one or more embodiments, the disclosed systems utilize machine and user defined tags, machine learning model predictions, and user input to generate a ground truth annotation that includes frame information in addition to intent, attribute, object, and/or location information. In at least one embodiment, the disclosed systems generate ground truth annotations in conformance with an annotation ontology that results in fast and accurate digital image editing dialogue annotation.