Grammaticality Classification for NLG Assistant Responses
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Solution Overview
Problem
Existing natural language generation systems face challenges in producing high-quality responses due to difficulties in controlling response quality, particularly in ensuring grammatical correctness and semantic accuracy, especially when using data-driven generators, which often produce ungrammatical or semantically incorrect outputs.
Innovation Solution
The implementation of a filtering and ranking framework that utilizes grammaticality, semantic correctness, and naturalness models, combined with supervised learning approaches like convolutional neural networks and gradient boosted decision trees, to filter out unacceptable responses and rank candidate responses based on classification models and rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data-driven natural language generation models are used to generate responses, then productivity and response variety are improved, but grammatical correctness and semantic accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary grammaticality classification model that sits between the data-driven NLG model and the user. This classifier evaluates generated responses and identifies ungrammatical ones, allowing the system to maintain high productivity while filtering out unreliable outputs. The classifier acts as a mediator that preserves the benefits of data-driven generation while mitigating its grammatical errors.
Solution Approach 2:
The patent replaces traditional rule-based grammatical checking mechanisms with a machine learning-based classification model. Instead of using rigid linguistic rules that limit response variety, the system uses a trained classifier that can evaluate grammaticality while allowing the flexible, data-driven NLG model to generate diverse responses. This substitution maintains productivity while improving grammatical reliability.
2Reliability
If traditional rule-based natural language generation is used, then grammatical correctness is improved, but response quality variety and naturalness deteriorate
Solution Approach 1:
The patent segments the response generation process into multiple independent components: a data-driven NLG model for generating diverse responses, a grammaticality classifier for evaluating them, and a selection mechanism for choosing the best response. This segmentation allows each component to specialize - the generator focuses on variety while the classifier ensures grammatical correctness - resolving the contradiction between response variety and grammatical reliability.
Solution Approach 2:
The grammaticality classifier serves as an intermediary that enables the system to use flexible data-driven generation without sacrificing grammatical correctness. It mediates between the ungrammatical outputs of the NLG model and the requirement for grammatically correct responses, allowing the system to maintain both response variety and grammatical reliability simultaneously.
3Measurement precision
If filtering and ranking frameworks with multiple classification models are implemented, then response quality precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the quality assessment process into distinct classification models for different aspects (grammaticality, semantic correctness, naturalness). Each model focuses on a specific dimension of quality, making the overall system more manageable and interpretable. This segmentation improves measurement precision while organizing complexity into modular, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces intermediary classification models that bridge the gap between raw generated responses and final quality assessment. These classifiers act as intermediate evaluation layers that break down the complex task of quality assessment into manageable sub-tasks, improving overall assessment accuracy while structuring the system complexity in a hierarchical, organized manner.
Data Source
AI summary
In one embodiment, a method includes receiving a user input from a client system associated with a user, generating a plurality of candidate responses by a natural-language generation module in response to the user input, determining a quality-indication by a filtering module for each candidate response of the plurality of candidate responses based on one or more classification models and one or more classification rules, wherein the classification models and classification rules are based on one or more acceptance-criteria, selecting one or more candidate responses from the plurality of candidate responses based on their respective quality-indications, ranking the selected candidate responses based on one or more ranking-criteria, and sending instructions for presenting a top-ranked candidate response to the user to the client system associated with the user responsive to receiving the user input.