Ensemble ML for Conversational Proficiency Scoring
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Solution Overview
Problem
Current dialog-based learning and assessment systems lack the ability to provide effective and actionable feedback to users, hindering their conversational proficiency improvement and widespread adoption.
Innovation Solution
An ensemble of machine learning models, including stacked bi-directional long short-term memory (BiLSTM) networks, End to End Memory Networks (MemN2N), and transformer-based models, are used to characterize conversational proficiency by extracting features from dialog data and fusing scores to generate a performance score, leveraging automated speech recognition and feature engineering techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated speech recognition and machine learning models are used to characterize dialog interaction, then the ability to provide actionable feedback is improved, but the system complexity increases
Solution Approach 1:
The system segments the dialog characterization task into multiple independent machine learning models, each trained to score specific dialog constructs (e.g., topic development, interaction, elaboration, structure). This segmentation allows each model to focus on a specific aspect of conversational proficiency, improving measurement precision while managing overall system complexity through modular architecture.
Solution Approach 2:
The system merges outputs from multiple specialized machine learning models into a comprehensive performance score using score fusion techniques (e.g., quadratic weighted kappa). This combination integrates insights from various models to provide a holistic characterization of conversational proficiency, achieving high measurement precision by leveraging the strengths of multiple models.
2Measurement precision
If multiple machine learning models are employed to score different dialog constructs, then the accuracy of proficiency assessment is improved, but the computational resources required increase
Solution Approach 1:
The assessment task is divided into multiple specialized models, each handling specific dialog constructs. This segmentation improves accuracy for each construct while allowing computational resources to be distributed and optimized across models, rather than requiring one massive computationally intensive model.
Solution Approach 2:
The system employs an ensemble of models where each model performs partial action (scoring specific constructs) rather than requiring a single model to perform all scoring functions. This partial action approach improves overall accuracy while managing computational resources by having each model focus on its specialized task.
3Productivity
If automated feedback systems are implemented at scale, then the accessibility of language learning is improved, but the quality of feedback may deteriorate
Solution Approach 1:
The system provides automated self-service feedback through machine learning models that analyze dialog interactions and generate performance scores without human intervention. This self-service capability enables the system to scale to many users simultaneously while maintaining consistent feedback quality, as the automated models apply the same rigorous assessment criteria to all users.
Solution Approach 2:
The system implements automated feedback loops where machine learning models continuously analyze user dialog interactions and provide actionable performance scores. This feedback mechanism maintains quality at scale by using trained models that apply consistent assessment standards, and the models can be retrained with new data to continuously improve feedback quality as more users are served.
Data Source
AI summary
Human-machine dialog is characterized by receiving data comprising a recording of an individual interacting with a dialog application simulating a conversation. Thereafter, the received data is parsed using automated speech recognition to result in text comprising a plurality of words. Features are extracted from the parsed data and then input an ensemble of different machine learning models each trained to generate a score characterizing a plurality of different dialog constructs. Thereafter, scores generated by the machine learning models for each of the dialog constructs are fused. A performance score is then generated based on the fused scores which characterizes a conversational proficiency of the individual interacting with the dialog application. Data can then be provided which includes or otherwise characterizes the generated score. Related apparatus, systems, techniques and articles are also described.