Conversational Text Annotation Through Agent Response Capture
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Solution Overview
Problem
Annotating utterances in conversational messaging is costly and labor-intensive, requiring manual human judgment, which is inefficient for large data sets needed to train machine learning models.
Innovation Solution
A method and system that implicitly annotate textual data by leveraging agents' responses in conversational messaging systems, where agents select pre-written or generated responses to user inputs, associating these responses with unique IDs, and recording them for training natural language intent classification models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human annotation is used to label utterances, then annotation accuracy and quality are improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system enables agents to self-annotate by automatically capturing their selected responses from the messaging interface and associating them with user utterances. This eliminates the need for separate manual annotation processes, as agents naturally perform annotation during their normal communication tasks.
Solution Approach 2:
The server acts as an intermediary that automatically captures agent responses, associates them with corresponding user utterances, and stores them as annotated data. This intermediary process transforms normal messaging interactions into useful training data without requiring additional manual intervention.
2Measurement precision
If manual human annotation is used to label utterances, then annotation quality is improved, but cost increases significantly
Solution Approach 1:
Agents perform the annotation function as part of their normal workflow by simply selecting responses from the messaging interface. The system captures these selections and uses them as annotated training data, eliminating the need to pay external annotators or allocate dedicated annotation resources.
Solution Approach 2:
The messaging interface serves multiple functions: it enables normal communication between agents and users, simultaneously captures annotated data for training, and stores the results in the database. This multi-functionality eliminates the need for separate annotation tools and processes.
3Measurement precision
If large data sets are gathered to train machine learning models, then model accuracy is improved, but the complexity and resource requirements increase
Solution Approach 1:
The system merges the messaging communication function with the data collection function into a single integrated process. Agent responses selected during normal messaging are automatically captured and stored as annotated training data, eliminating the need for separate data collection systems.
Solution Approach 2:
The system continuously captures annotated data as agents communicate with users in real-time. This ongoing process ensures that training data is constantly being updated and expanded without requiring separate batch processing or periodic data collection campaigns.
Data Source
AI summary
Apparatuses, methods, and systems for automated testing and selection of multiple templates of a form. One method includes receiving, by a server, a first input text message from a user, displaying, by the server, the first input text message to an agent, displaying, by the server, a configurable menu of responses to the first input text message to the agent, receiving, by the server, a selection of one of the configurable menu of responses from the agent, facilitating, by the server, sending of the selected of one of the configurable menu of responses to the user, and associating and recording the selected one of the configurable menu of responses with the first input text message.