Incorporating External Knowledge into Machine Reading for Generative Question Answering

TL;DR

This paper introduces KEAG, a neural model that effectively integrates external knowledge sources to generate more accurate and contextually relevant natural language answers in question answering tasks.

Contribution

The paper presents KEAG, a novel neural architecture that adaptively leverages external knowledge during answer generation, improving over existing knowledge-aware models.

Findings

KEAG outperforms models without knowledge in answer quality.

KEAG surpasses existing knowledge-aware models on benchmark datasets.

The model effectively determines when and which external facts to use.

Abstract

Commonsense and background knowledge is required for a QA model to answer many nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context. In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge. During the process of answer generation, KEAG adaptively determines when to utilize symbolic knowledge and which fact from the knowledge is useful. This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. The empirical study on public benchmark of answer generation demonstrates that KEAG improves answer quality over models without knowledge and existing knowledge-aware models, confirming its effectiveness in leveraging knowledge.