Hybrid Schema-Template Text Generation for Dialogue Systems
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Solution Overview
Problem
Virtual assistants struggle to generate coherent and fluent responses when handling multiple pieces of information from different APIs, especially when dealing with new or updated APIs, and traditional data-driven and template-based approaches are costly and impractical for cross-domain performance.
Innovation Solution
A hybrid approach combining schema-guided and template-guided text generation, where dialogue acts are modified with natural language descriptions and concatenated templated responses are smoothed by a sequence-to-sequence transformer model, allowing for efficient and accurate natural language response generation across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional data-driven approach with a pretrained NLG model is used to transform dialogue acts into natural language utterances, then the system can generate responses based on API data, but the model fails to correctly interpret dialogue acts based on new or updated APIs particularly when the subject matter differs from the domains on which the NLG model was trained
Solution Approach 1:
The patent introduces schema-guided dialogue acts as an intermediary layer between the API data and the NLG model. By replacing slot names with natural language descriptions from schemas, the system creates a more interpretable intermediate representation that bridges the gap between structured API responses and the pretrained NLG model's expectations, enabling better cross-domain performance without retraining
Solution Approach 2:
The patent modifies the input parameters to the NLG model by transforming dialogue acts with slot names into dialogue acts with natural language descriptions. This parameter transformation allows the model to process new API domains more effectively by using semantically richer inputs that align better with the model's pretrained knowledge
2Measurement precision
If the NLG model is fine-tuned on a large amount of annotated text to correctly infer slots and values, then the transformation accuracy improves, but the training cost in terms of time and computing resources increases
Solution Approach 1:
The patent performs preliminary action by using schemas to pre-generate natural language descriptions for slot names before feeding dialogue acts to the NLG model. This preprocessing step prepares the input data in advance, allowing the pretrained model to work with already-optimized inputs without requiring additional fine-tuning on large annotated datasets
Solution Approach 2:
The patent uses schema definitions as templates to generate natural language descriptions that copy and adapt to different API domains. Instead of training the model on domain-specific data, the schema-based approach reuses generic schema patterns across different domains, eliminating the need for extensive domain-specific training
3Ease of operation
If template-based NLG systems are used to generate responses based on human-generated templates, then the responses are fluent and predictable, but the system does not scale well when combining disparate pieces of information from multiple APIs as additional templates must be created to cover every type of combined response
Solution Approach 1:
The patent makes the schema-guided dialogue act system universal by using schemas that can represent different types of information from multiple APIs in a unified way. Instead of creating separate templates for each API combination, the schema-based approach provides a universal framework that adapts to different domains and API responses through natural language descriptions
Solution Approach 2:
The patent replaces the mechanical template-based system with a more flexible schema-guided approach. Instead of rigidly matching dialogue acts to predefined templates, the system uses schemas to generate natural language descriptions that guide the pretrained NLG model, substituting the mechanical template-matching process with a more adaptive description-based approach
4Loss of information
If the system concatenates multiple pieces of information from different APIs to handle complex user requests, then the response completeness improves, but the coherence and fluency of the combined response deteriorates
Solution Approach 1:
The patent merges multiple pieces of information from different APIs by transforming them into a unified set of schema-guided dialogue acts. All API responses are converted to the same dialogue act format with natural language descriptions, allowing the pretrained NLG model to process and combine them coherently in a single pass, maintaining both completeness and fluency
Data Source
AI summary
Systems and methods for guided text generation in task-based dialogue. In some aspects of the technology, an automated assistant system is configured to receive a user request, call multiple APIs, generate dialogue acts based on data received from each API, replace any slot names in the dialogue acts with natural language descriptions of the slots, concatenate the modified dialogue acts, and pass the concatenated result to an NLG model for generation of a natural language response. In some aspects of the technology, the automated assistant may be configured to generate simple templated responses based on the data received from each API, concatenate the simple templated responses, and pass the concatenated sequence to an NLG model trained as a sequence-to-sequence transformer for generation of a final natural language response.