Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering (1911.03868v2)

Abstract: We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article. Our goals are to boost coverage by using knowledge-guided retrieval to find more relevant passages than text-matching methods, and to improve accuracy by allowing for better knowledge-guided fusion of information across related passages. Our graph retrieval method expands a set of seed keyword-retrieved passages by traversing the graph structure of the knowledge base. Our reader extends a BERT-based architecture and updates passage representations by propagating information from related passages and their relations, instead of reading each passage in isolation. Experiments on three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA, show improved performance over non-graph baselines by 2-11% absolute. Our approach also matches or exceeds the state-of-the-art in every case, without using an expensive end-to-end training regime.

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

The paper "Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering" presents a novel approach for enhancing the efficacy of open-domain question answering (QA) systems by leveraging a passage graph structure. This approach is fundamentally designed to increase the precision and coverage of retrieved information and to optimize the fusion of knowledge from related passages.

Methodology Overview

At the core of the proposed framework is the utilization of a passage graph. In this graph, vertices correspond to textual passages, and edges signify relationships derived either from an external knowledge base or through co-occurrence within the same document. The design intention here is twofold:

1. Knowledge-Guided Retrieval: To surpass traditional text-matching methods by retrieving a wider array of relevant passages.
2. Information Fusion: To enhance accuracy through synergistic integration of data from interrelated passages.

The retrieval process is initiated with a set of seed passages, acquired using keyword-matching techniques. This set is subsequently expanded by navigating the graph structure of a knowledge base, thereby discovering pertinent yet initially overlooked passages. In terms of reading, the paper extends the BERT-based architecture by reformulating passage representations, which involves propagating data along the graph's connections. This is opposed to the conventional approach of analyzing each passage in isolation.

Experimental Results

This methodology was empirically validated across three prominent open-domain QA datasets: WebQuestions, Natural Questions, and TriviaQA. The findings reveal an improvement in performance metrics, ranging from a 2% to 11% absolute increase over conventional non-graph-based approaches. Moreover, the proposed system not only aligns with but often surpasses the state-of-the-art benchmarks in each dataset. Remarkably, these achievements were realized without the need for an exorbitant end-to-end training process, which is typically resource-intensive.

Implications and Future Directions

The introduction of this knowledge-guided QA framework bears significant implications for both theoretical exploration and practical application:

• Theoretical Insights: By demonstrating the benefits of graph-based architectures in information retrieval and synthesis, this research advocates for a paradigm shift in the design of QA systems. It prompts further investigation into optimizing graph traversal algorithms and relation extraction frameworks.
• Practical Applications: The results suggest potential for enhanced performance in real-world applications, including digital assistants and information retrieval systems used in professional contexts.

Looking forward, the implications of integrating AI with knowledge-guided mechanisms are profound, particularly in fields that rely heavily on large-scale information retrieval. Future research efforts could focus on refining the efficiency of the graph traversal process and exploring broader applications of this model in different domains. Additionally, the potential for merging this approach with other machine learning techniques, such as reinforcement learning or unsupervised learning, could offer unexplored avenues for further advancement in AI-driven QA systems.