Hybrid Sequence-to-Sequence Natural Language Generation
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Solution Overview
Problem
Existing natural language generation systems for dialog systems require extensive manual annotations and explicit structured representations of dialog acts, which are time-consuming and require linguistic expertise, limiting their efficiency and scalability.
Innovation Solution
A method and system for natural language generation that uses a trained model to assign utterance labels to text sequences based on extracted features, allowing for the generation of natural language utterances without the need for explicit structured representations, utilizing a combination of Natural Language Processing tools and annotation-based machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit structured representations of dialog acts are used, then generation accuracy is improved, but annotation time and linguistic expertise requirements increase
Solution Approach 1:
The patent uses sequence tags from training data to copy and guide the generation process, rather than requiring explicit structured representations. The model learns to generate sequences by copying patterns from annotated examples, reducing the need for time-consuming manual annotation while maintaining generation accuracy through learned statistical patterns.
Solution Approach 2:
The patent replaces the mechanical annotation process with a machine learning model that automatically learns generation patterns. Instead of manually creating structured representations, the system uses a neural network model trained on sequential data to automatically generate appropriate responses, substituting automated learning for manual annotation work.
2Measurement precision
If explicit structured representations of dialog acts are used, then generation accuracy is improved, but system complexity increases
Solution Approach 1:
The system copies sequential patterns from training data directly into the model, avoiding the need for complex structured representation systems. By learning sequences through copying patterns in the training data, the system achieves accurate generation with simpler architecture that doesn't require explicit structured dialog act representations.
Solution Approach 2:
The patent changes the approach from using explicit structured parameters to using sequential token representations. The model processes dialog acts as sequences of tokens rather than structured objects, simplifying the system while maintaining accuracy through the power of sequence-to-sequence learning.
3Ease of operation
If traditional NLG components are used, then grammatical text generation is achieved, but handling text snippets with paucity of internal structures is difficult
Solution Approach 1:
The patent copies complete sequence patterns from training data that include both the input text snippets and their corresponding generated responses. This allows the model to learn how to handle snippets with paucity of internal structures by copying successful generation patterns, avoiding the need for complex structural analysis of each snippet.
Solution Approach 2:
The model performs preliminary learning during training by exposure to many examples of text snippets and their appropriate generations. This preliminary action of learning during training enables the system to handle snippets with minimal internal structure during deployment, as the patterns have already been learned in advance.
Data Source
AI summary
A method and method for natural language generation employ a natural language generation model which has been trained to assign an utterance label to a new text sequence, based on features extracted from the text sequence, such as parts-of-speech. The model assigns an utterance label to the new text sequence, based on the extracted features. The utterance label is used to guide the generation of a natural language utterance, such as a question, from the new text sequence. The system and method find application in dialog systems for generating utterances, to be sent to a user, from brief descriptions of problems or solutions in a knowledge base.