Recursive Reasoning Unit for Interpretable Word Representations
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Solution Overview
Problem
Existing word representation systems face challenges in domain adaptation, requiring large numbers of labels and lacking interpretability, as they struggle to justify word similarities in vector spaces, making it difficult to trust downstream machine learning models in high-stake domains.
Innovation Solution
A recursive reasoning unit (RRU) is trained using a knowledge graph and co-occurrence matrix to discover reasoning paths between words, predicting word co-occurrence frequency and updating model parameters, allowing for label-efficient domain adaptation and providing human-friendly justifications for word closeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing word representation systems (word2vec, BERT) are used, then word vector representations can be obtained, but large numbers of labels are required for domain adaptation and the learned vector space lacks interpretability
Solution Approach 1:
The patent introduces commonsense knowledge graphs as an intermediary between training data and word representation learning. The knowledge graph provides structured semantic relationships that guide the learning process, enabling the model to adapt to new domains without requiring large numbers of labeled examples. The knowledge graph acts as a mediator that transfers commonsense reasoning capabilities to the word representation system.
Solution Approach 2:
The patent performs preliminary construction of commonsense knowledge graphs before domain adaptation. By pre-organizing semantic relationships and commonsense knowledge in the knowledge graph, the system prepares a structured foundation that enables efficient adaptation to new domains with minimal labeling required. This preliminary structuring of knowledge reduces the labeling burden during subsequent domain adaptation.
2Measurement precision
If existing word representation systems are used, then word similarity can be computed, but the systems cannot justify why words are similar
Solution Approach 1:
The patent adds a new dimension of interpretability to word similarity measurement by incorporating knowledge graph paths as a separate dimension alongside vector similarity. Instead of relying solely on numerical vector distances, the system provides path-based explanations that traverse the knowledge graph, offering human-understandable justifications for why words are similar while maintaining precise similarity measurements.
Solution Approach 2:
The knowledge graph serves as an intermediary that bridges the gap between computational word similarity and human interpretable reasoning. By routing similarity computations through the structured relationships in the knowledge graph, the system produces both accurate similarity measurements and explanatory paths that show the reasoning behind the similarities.
3Reliability
If labeled data is collected for domain adaptation, then model performance improves, but the process becomes expensive and impractical for many applications
Solution Approach 1:
The patent enables the system to self-adapt to new domains by leveraging the structured knowledge in the commonsense knowledge graph. Instead of requiring external labeled data for adaptation, the system uses the pre-organized semantic relationships to automatically adjust word representations to new domains, making the adaptation process self-sufficient and eliminating the need for expensive manual labeling.
Solution Approach 2:
The commonsense knowledge graph serves multiple functions: it provides semantic relationships for word representation learning, offers reasoning paths for interpretability, and enables domain adaptation without labeled data. This multi-functional use of the knowledge graph structure allows the system to achieve reliable performance across different domains while avoiding the cost of label collection.
Data Source
AI summary
A method trains a recursive reasoning unit (RRU). The method receives a graph for a set of words and a matrix for a different set of words. The graph maps each word in the set of words to a node with node label and indicates a relation between adjacent nodes by an edge with edge label. The matrix indicates word co-occurrence frequency of the different set of words. The method discovers, by the RRU, reasoning paths from the graph for word pairs by mapping word pairs from the set of words into a source word and a destination word and finding the reasoning paths therebetween. The method predicts word co-occurrence frequency using the reasoning paths. The method updates, responsive to the word co-occurrence frequency, model parameters of the RRU until a difference between a predicted and true word occurrence are less than a threshold amount to provide a trained RRU.