Utterance Annotation Interface for Training Data Generation
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Solution Overview
Problem
The existing methods for training computerized assistants require large numbers of annotated utterances, which is a time-consuming and expertise-dependent process, limiting the pool of annotators and the efficiency of accumulating suitable training data.
Innovation Solution
A training pipeline that includes an utterance annotation interface with a hierarchical menu, allowing human annotators with less experience to select utterance annotations, and automatically generates variants of dialogues to expand the coverage of training data, facilitating quicker and more efficient data accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of utterances is performed by human annotators, then training data can be created for machine learning models, but the process is time-consuming and requires specialized expertise
Solution Approach 1:
The patent introduces an automated annotation system that acts as an intermediary between the training data requirements and human annotators. The system pre-processes utterances, provides annotation suggestions, and guides annotators through standardized workflows, reducing both the time required and the expertise needed while maintaining annotation quality
Solution Approach 2:
The system creates templates and reusable annotation patterns from existing labeled data. These templates serve as copies that can be applied to similar utterances, reducing the time and expertise required for each individual annotation while maintaining consistency and accuracy
2Measurement precision
If the annotator pool is limited to experts, then annotation quality can be maintained, but the efficiency of accumulating training data is reduced
Solution Approach 1:
The annotation system incorporates self-service features including automated preprocessing, suggestion generation, and quality validation. These features enable less experienced annotators to perform high-quality work independently, expanding the annotator pool while maintaining annotation standards
Solution Approach 2:
The system implements feedback mechanisms that provide real-time guidance to annotators, show examples of correct annotations, and validate entries against quality standards. This feedback loop enables less experienced annotators to maintain high quality while increasing overall productivity
3Reliability
If large numbers of annotated utterances are collected, then machine learning model performance improves, but the expertise-dependent annotation process becomes a bottleneck
Solution Approach 1:
The annotation process is segmented into distinct, manageable steps with automated assistance at each stage. Utterances are processed through preprocessing, suggestion generation, annotation, and validation stages, reducing the complexity burden on human annotators while enabling scalable data collection
Solution Approach 2:
The patent replaces the manual, expertise-intensive annotation mechanism with an automated system that uses machine learning models to pre-annotate and guide human reviewers. This substitution reduces process complexity while maintaining or improving annotation quality and enabling faster data accumulation
Data Source
AI summary
A computing device includes a display configured to present a graphical user interface. The graphical user interface includes a transcript portion configured to display an unannotated transcript representing an ordered sequence of one or more dialogue events involving a client and a computerized assistant, at least one of the dialogue events taking the form of an example client utterance, and an annotation portion configured to display a hierarchical menu including a plurality of candidate utterance annotations. An utterance annotation machine is configured to receive one or more computer inputs selecting, for each of one or more response parameters in the example client utterance, utterance annotations from the hierarchical menu that collectively define a machine-readable interpretation of the example client utterance. An annotated utterance having a predetermined format usable to train the computerized assistant is output to a data store based on the example client utterance.