Generation-Augmented Ranking for Knowledge Base Question Answering
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Solution Overview
Problem
Existing knowledge base question answering systems struggle to handle questions involving unseen compositions or schema items, as they often fail to generalize beyond the data they have been trained on, leading to poor performance in answering queries with novel combinations of schema elements.
Innovation Solution
A generation-augmented iterative ranking approach is employed, where a ranking model selects semantically coherent logical forms from a knowledge graph, and a generation model refines these forms to create a target logical form that can be executed on the knowledge base, leveraging pre-trained language models like BERT and T5 to handle unseen schema items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ranking-based approaches are used to query the knowledge base, then the system can provide answers for seen schema items, but it fails to handle unseen compositions or schema items
Solution Approach 1:
The system segments the question answering process into two distinct stages: a ranking stage that handles seen schema items using traditional methods, and a generation stage that handles unseen compositions using a language model. This segmentation allows each stage to specialize in its respective task, improving overall adaptability while maintaining reliability on both seen and unseen data.
Solution Approach 2:
A language model serves as an intermediary component between the ranking model and the final answer generation. The language model receives ranked logical forms as input and generates answers for unseen compositions, acting as a mediator that bridges the gap between traditional ranking approaches and the need for compositional generalization.
2Quantity of substance
If the knowledge base is made extremely large in scale to provide comprehensive world knowledge, then more knowledge is available, but the database becomes difficult to interact with requiring designated tools
Solution Approach 1:
The system replaces complex mechanical query languages (such as SPARQL) with a natural language interface. Users can ask questions in plain English, and the system automatically translates these into logical forms that can be executed against the knowledge base, eliminating the need for users to learn and use designated query tools.
Solution Approach 2:
The system introduces an intermediary translation layer that converts natural language questions into executable logical forms. This intermediary mechanism simplifies the interaction interface while maintaining the ability to query the large-scale knowledge base, making the system easier to operate without sacrificing access to comprehensive knowledge.
3Adaptability or versatility
If existing question answering systems query over the knowledge base using predefined rules, then they can generate answers for training data, but they cannot generalize to questions with novel combinations of schema elements
Solution Approach 1:
The system changes the fundamental parameter of how logical forms are generated: instead of using fixed predefined rules, the system employs a language model that can generate logical forms based on the semantic meaning of the question. This parameter change enables the system to handle novel combinations of schema elements while maintaining high precision through the language model's ability to accurately translate natural language into logical representations.
Data Source
AI summary
Embodiments described herein provide a question answering approach that answers a question by generating an executable logical form. First, a ranking model is used to select a set of good logical forms from a pool of logical forms obtained by searching over a knowledge graph. The selected logical forms are good in the sense that they are close to (or exactly match, in some cases) the intents in the question and final desired logical form. Next, a generation model is adopted conditioned on the question as well as the selected logical forms to generate the target logical form and execute it to obtain the final answer. For example, at inference stage, when a question is received, a matching logical form is identified from the question, based on which the final answer can be generated based on the node that is associated with the matching logical form in the knowledge base.