Utterance Pairing for NLP Prediction Accuracy
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Solution Overview
Problem
Standard Natural Language Processing (NLP) techniques applied to transcripts of spoken conversations yield suboptimal results due to the incomplete and grammatically incorrect nature of utterances, which lack context, leading to inaccurate predictions.
Innovation Solution
The method involves pairing each utterance with both its preceding and following utterances to create context-rich data pairs for training machine learning models, effectively doubling the training data and improving model performance by using sentence embeddings and machine learning algorithms to generate accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard NLP techniques are applied to transcripts of spoken conversations, then the processing can be performed using existing models, but the accuracy of predictions deteriorates due to the incomplete and grammatically incorrect nature of utterances
Solution Approach 1:
The patent segments the conversation transcript into individual utterances and pairs each with its preceding and following utterances to create context-rich data units. This segmentation allows the model to process conversations in manageable chunks while preserving contextual information, resolving the contradiction between using existing models and achieving accurate predictions.
Solution Approach 2:
The patent adds a contextual dimension by pairing each utterance with its surrounding utterances (previous and next). This transforms single utterance processing into multi-utterance context processing, enabling standard NLP models to achieve speech-level accuracy without requiring completely new model architecture.
2Measurement precision
If utterances are processed as complete sentences with grammatical structure, then standard NLP models can be used, but the model performance deteriorates because conversational utterances are incomplete and lack grammatical correctness
Solution Approach 1:
The patent performs preliminary context assembly by pairing each utterance with its preceding and following utterances before feeding data to the model. This preliminary action creates grammatically complete contextual units that standard NLP models can process effectively, improving transcription accuracy without requiring models specifically adapted to conversational styles.
Solution Approach 2:
The paired utterances act as intermediaries that bridge the gap between incomplete conversational utterances and the expectations of standard NLP models. By providing contextual surrounding utterances, the system mediates between the informal nature of speech and the formal requirements of NLP processing.
3Reliability
If context is added to each utterance by pairing with surrounding utterances, then the model training performance improves, but the quantity of data processing and computation increases
Solution Approach 1:
The patent merges multiple utterances into paired contexts, effectively combining the information from surrounding utterances with the target utterance. This merging approach enriches the training data with contextual information without requiring separate processing of each utterance, thereby improving model performance while managing data processing requirements.
Solution Approach 2:
The paired utterance structure serves multiple functions: it provides contextual information, maintains grammatical completeness, and creates training data in a standardized format. This multi-functionality allows a single processing approach to simultaneously improve model training performance and manage data quantity requirements.
Data Source
AI summary
A machine-learned model receives an audio signal representing a spoken sequence of utterances for classification. The model repeatedly processes utterance pairs in a sequence of (u0, u1), (u1, u2), . . . (un-1, un) to generate prediction of target utterances. Each of the utterance pairs comprises a target utterance paired with another one of the utterances.