Stop Word Data Augmentation for NLP Intent Classification
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Solution Overview
Problem
Current chatbot systems face challenges in accurately classifying user intents due to inadequate training data, often misclassifying utterances when stop words are not adequately represented, leading to incorrect intent determination.
Innovation Solution
The implementation of stop word data augmentation techniques, where existing stop word patterns in training samples are preserved, and non-stop words are randomly replaced with stop words or phrases, generating augmented training data to improve intent classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If stop words are removed or not adequately represented in training data, then the model training process becomes simpler, but the intent classification accuracy deteriorates as the model picks up on incorrect stop word patterns
Solution Approach 1:
The patent applies preliminary action by proactively generating augmented training data with stop words before the model training begins. The data augmentation process replaces non-stop words with stop words or phrases in advance, creating a prepared training set that prevents the model from learning incorrect stop word patterns during training, thus resolving the contradiction between training simplicity and classification accuracy
Solution Approach 2:
The patent changes the parameter composition of training data by systematically replacing non-stop words with stop words or phrases at controlled ratios (e.g., 10-50% replacement). This parameter change in the training data composition enables the model to learn correct intent patterns while ignoring stop words, improving intent classification accuracy without complicating the training process
2Reliability
If more training data is added to improve model performance, then the model generalization improves, but the data preparation complexity and time increase
Solution Approach 1:
The patent applies copying by generating augmented training data through systematic transformation of existing training utterances. Instead of collecting and preparing extensive new data, the method creates multiple augmented versions of existing data by replacing non-stop words with stop words or phrases, thus improving model generalization while minimizing additional data preparation time
Solution Approach 2:
The patent changes parameters of existing training data (replacing non-stop words with stop words at various ratios) to generate diverse augmented training sets. This parameter-based transformation approach efficiently expands training data volume and variety without requiring proportional increases in data collection and preparation time
Data Source
AI summary
Techniques for stop word data augmentation for training chatbot systems in natural language processing. In one particular aspect, a computer-implemented method includes receiving a training set of utterances for training an intent classifier to identify one or more intents for one or more utterances; augmenting the training set of utterances with stop words to generate an augmented training set of out-of-domain utterances for an unresolved intent category corresponding to an unresolved intent; and training the intent classifier using the training set of utterances and the augmented training set of out-of-domain utterances. The augmenting includes: selecting one or more utterances from the training set of utterances, and for each selected utterance, preserving existing stop words within the utterance and replacing at least one non-stop word within the utterance with a stop word or stop word phrase selected from a list of stop words to generate an out-of-domain utterance.