A Neural Network-based Framework for Non-factoid Question Answering

Abstract

In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.