A Machine Reading Comprehension-Based Approach for Featured Snippet Extraction

Abstract

The extraction of featured snippet can be considered as the problem of Question Answering (QA). This paper presents a featured snippet extraction system by employing a technique of machine reading comprehension (MRC). Specifically, we first analyze the characteristics of questions with different types and their corresponding answers. Then, we classify a given question into various types, which is incorporated as key features in the subsequent model configuration. Based on that, we present a model to extract the candidate passages from recalled documents in a MRC fashion. Next, a novel MRC model with multiple stages of attention is proposed to extract answers from the selected passages. Last, in the answer re-ranking stage, we design a question type-adaptive model to produce the final answer. The experimental results on two open-domain QA Datasets clearly validate the effectiveness of our system and models in featured snippet extraction.