NLP Model Training With Modified Cross-Entropy for Incomplete Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The bottleneck in training natural language processing (NLP) models is the inefficiency and inconsistency in labeling large datasets due to increased cognitive load and the need for extensive annotation, especially when label sets are large and dynamic, leading to inconsistencies and the requirement for repetitive relabeling.
Innovation Solution
A method that modifies the cross entropy loss function to accommodate incomplete annotation data by defining subset-specific loss terms, allowing annotators to focus on a reduced set of classes, handling ambiguous labels, and using hierarchical tree structures to organize classes, thereby reducing annotation burden and enabling efficient training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If annotators label every word in text fragments with a large label set, then the NLP model can be trained comprehensively, but the cognitive load and time required for annotation increase significantly
Solution Approach 1:
The patent divides the annotation task into segments by introducing partial labels that allow annotators to mark only certain words as belonging to specific classes, rather than requiring complete classification of every word. This segmentation reduces the cognitive load and time required while still providing sufficient training data for the NLP model.
Solution Approach 2:
The patent applies partial action by allowing incomplete annotations where not all words need to be labeled. The modified cross-entropy loss function accommodates these partial labels by treating unlabeled words as neutral, enabling the model to learn from the provided annotations without requiring exhaustive labeling of every word in the text.
2Adaptability or versatility
If the label set is expanded to include more classes, then the model's classification capability improves, but the annotation quality and consistency decrease due to increased cognitive load
Solution Approach 1:
The patent segments the classification task by allowing annotators to focus on labeling only the words they are confident about, rather than forcing them to classify every word across a large label set. This reduces errors and maintains quality even when the label set is expansive.
Solution Approach 2:
The patent introduces a disposable neutral category that allows annotators to skip words they cannot confidently classify. This 'disposable' option prevents forced incorrect annotations and maintains overall data quality while allowing the label set to remain large and versatile.
3Adaptability or versatility
If the label set is revised over time with new labels added, then the model adapts to new classification needs, but previously labeled data becomes inconsistent and requires relabeling
Solution Approach 1:
The patent prepares the annotation system in advance to handle label set changes by using a modified loss function that can accommodate both old and new labels. Previously labeled data with partial labels remains valid and can be combined with newly labeled data, eliminating the need for relabeling when the label set evolves.
Solution Approach 2:
The patent creates a universal annotation framework where partial labels and the neutral category work across different label set versions. This multi-functional approach allows the same annotation format to serve both historical and updated classification schemes, maintaining productivity during label set revisions.
4Loss of information
If complete annotation of all words is required, then the training data is comprehensive, but the annotation cost and complexity increase
Solution Approach 1:
The patent deliberately uses partial annotation where only certain words are labeled while others remain unlabeled. The modified cross-entropy loss function processes these partial annotations effectively, maintaining training data utility without requiring the complexity of complete word-by-word annotation.
Data Source
AI summary
Methods of training a natural language processing (NLP) model comprise storing classification data associated with a plurality of words, defining model input data associated with each of the plurality of words, evaluating a cross entropy loss function to derive a loss value, and adjusting the NLP model according to the loss value derived from the cross entropy loss function. The cross entropy loss function may be defined to include one or more special loss terms to support training of the NLP model on the basis of incomplete classification data, such as in cases where not all possible classes are considered by the annotator, where not all words are provided with annotated classes, where consensus has not been reached between annotators, and/or where not all classes have been defined.