Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals (2101.03737v2)

Abstract: Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowledge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback from the final answer, which makes the learning unstable or ineffective. To address this challenge, we propose a novel teacher-student approach for the multi-hop KBQA task. In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network. The major novelty lies in the design of the teacher network, where we utilize both forward and backward reasoning to enhance the learning of intermediate entity distributions. By considering bidirectional reasoning, the teacher network can produce more reliable intermediate supervision signals, which can alleviate the issue of spurious reasoning. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our approach on the KBQA task. The code to reproduce our analysis is available at https://github.com/RichardHGL/WSDM2021_NSM.

• The paper introduces a teacher-student framework that leverages bidirectional reasoning to supply reliable intermediate supervision signals for KBQA.
• It employs a dual network design combining an adapted NSM and graph neural networks to iteratively refine multi-hop reasoning over diverse datasets.
• Experimental results on MetaQA, WebQuestionsSP, and CWQ show significant improvements over previous methods in complex multi-hop query scenarios.

Analysis of "Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals"

This paper addresses a notable challenge in Multi-hop Knowledge Base Question Answering (KBQA): the dearth of supervision signals at intermediate reasoning steps, which typically hampers the learning process. The authors propose a novel teacher-student network approach to enhance multi-hop KBQA by inferring intermediate supervision signals, which leads to a significant improvement in the reasoning capabilities of the student network. This is achieved through an ingenious design of the teacher network that leverages both forward and backward reasoning mechanisms for better learning of intermediate entity distributions.

Methodology Overview

The paper introduces a dual-focus network configuration:

1. The Student Network: This is charged with the primary task of identifying the correct answer entity. It uses an adapted version of the Neural State Machine (NSM), originally conceived for visual question answering on scene graphs. The extension involves integrating multi-relational knowledge bases and processing through graph neural networks to maintain progressive entity distributions over multiple reasoning hops. The NSM's reasoning component dynamically assesses the graph state by combining the instruction vectors focused on parts of the query and uses them for iterative updates over graph entities.
2. The Teacher Network: Its principal role is the generation of reliable intermediate supervision signals, aiding in overcoming the spurious reasoning challenge. The teacher network innovatively deploys bidirectional reasoning using two key architectures: parallel and hybrid reasoning. Parallel reasoning involves distinct forward and backward NSMs, whereas hybrid reasoning merges the reasoning flows, sharing the instruction vectors and the end-state information, which helps in tracing the reasoning paths with increased granularity.

Experiments and Results

The paper conducts comprehensive experiments using MetaQA, WebQuestionsSP (webqsp), and Complex WebQuestions (CWQ) datasets. It reports that the proposed teacher-student framework significantly enhances reasoning performance, particularly in complex KBQA scenarios requiring multi-hop reasoning. NSM variants with bidirectional reasoning outperform existing state-of-the-art methods like GraftNet and PullNet, especially on the webqsp and CWQ datasets, which involve intricate multi-hop queries. Notably, the NSM coupled with hybrid reasoning consistently improves intermediate entity discovery, demonstrated through ablation studies using reduced dataset size for robustness checks.

Implications and Future Directions

The implications of this research extend both practically and theoretically. Practically, the framework can enhance systems involving natural language interfaces over knowledge bases by providing improved interpretability and accuracy in answers that require multi-step deductions. Theoretically, the authors’ exploitation of self-supervision via bidirectional search strategies introduces new avenues for learning from weakly supervised mechanisms in task formulations beyond KBQA.

Future developments might include transferring the bidirectional reasoning paradigm into other graph-based reasoning tasks, enriching the student network with expressive KB embeddings, and enhancing the current format by incorporating additional learning paradigms to further distill multi-hop reasoning knowledge.

In conclusion, this paper underscores a methodical advance in KBQA systems through a thoughtfully designed teacher-student framework. By focusing on intermediate entity understanding and joining dual-directional reasoning, the authors open pathways for further innovations in automated reasoning over structured knowledge bases.