Single-Speaker Document Dialog Generation with Inferred Prompts
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Solution Overview
Problem
The scarcity of high-quality, expert-created conversational training data hinders the development of conversational question answering systems, as existing data sets are limited and often contain personal anecdotes and subjective opinions, lacking reliable sources and subject-matter expertise.
Innovation Solution
A method is employed to generate synthetic dialog training data by transforming single-speaker electronic documents into two-party conversations using a machine-learned inpainting model, predicting missing prompts to create coherent and evidence-backed dialogues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conversational training data is collected from internet forums and message boards, then the quantity of training data increases, but the quality decreases due to personal anecdotes and subjective opinions without reliable sources
Solution Approach 1:
The patent creates synthetic conversational data by copying and transforming information from single-speaker documents into multi-speaker dialog formats. The inpainting model generates hypothetical questions that would elicit the documented answers, producing training data that mirrors expert knowledge without requiring actual expert participants. This resolves the contradiction by providing abundant training data (quantity) that is derived from reliable source documents (quality).
Solution Approach 2:
The system uses the content within the documents themselves to generate the training data. The documents serve as both the knowledge source and the answer bank, with the inpainting model automatically creating the question-answer pairs. This self-service approach eliminates the need for external expert participants while maintaining high data quality and generating sufficient quantity for training.
2Reliability
If expert-created conversational data is obtained through crowd-sourcing, then the quality of training data improves with subject-matter expertise, but the quantity remains limited and costs increase
Solution Approach 1:
Instead of recruiting multiple expert participants for crowd-sourcing, the patent copies information from existing expert-authored documents and transforms it into conversational format. The inpainting model generates multiple hypothetical dialogues from a single document, multiplying the effective use of expert knowledge without incurring additional expert participation costs, thus achieving both high quality and increased quantity.
Solution Approach 2:
The patent performs preliminary action by using pre-existing expert documents as the foundation for training data generation. Rather than conducting time-consuming expert interviews or crowd-sourcing sessions, the system starts with already-written expert content and uses the inpainting model to pre-generate the conversational structures, efficiently producing large quantities of high-quality training data.
3Productivity
If existing conversational data sets are used for training, then the development process is faster, but the data lacks depth and informative content
Solution Approach 1:
The patent copies dense information from expert documents and structures it into conversational format, preserving the depth and informative content of the source material while making it suitable for training ConvQA systems. This approach maintains information density unlike shallow crowd-sourced data, while still enabling efficient automated generation for fast development.
Solution Approach 2:
The inpainting model changes the structural parameters of the data by transforming static document text into dynamic question-answer dialogues. This transformation preserves the informational content while adapting it to the conversational format required for training, achieving both speed and depth.
Data Source
AI summary
Provided are systems, methods, and machine learning models for generating synthetic dialog training data using a single-speaker electronic document. The method includes receiving an electronic document and performing natural language processing on the electronic document to obtain a plurality of utterances. The method also includes, for each utterance of the plurality of utterances, generating, using a machine-learned inpainting model, an inferred prompt for which the utterance is an answer, storing each utterance and the associated inferred prompt as a data item for the dialog training set of data items.