Long-Term Memory Networks for Question Answering

TL;DR

This paper introduces the Long-Term Memory Network (LTMN), a neural architecture that combines external memory and LSTM components to generate multi-word answers in question answering tasks, achieving state-of-the-art results.

Contribution

The paper presents the LTMN model, capable of multi-word answer generation and trained end-to-end with minimal supervision, advancing question answering systems.

Findings

Achieves state-of-the-art performance on synthetic and real-world datasets.

Can generate multi-word answers, unlike previous models.

Requires less supervision for training.

Abstract

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task. Several deep neural network architectures have been developed recently, which employ memory and inference components to memorize and reason over text information, and generate answers to questions. However, a major drawback of many such models is that they are capable of only generating single-word answers. In addition, they require large amount of training data to generate accurate answers. In this paper, we introduce the Long-Term Memory Network (LTMN), which incorporates both an external memory module and a Long Short-Term Memory (LSTM) module to comprehend the input data and generate multi-word answers. The LTMN model can be trained end-to-end using back-propagation and requires minimal supervision. We test our model on two synthetic data sets (based on Facebook's bAbI data set) and the real-world Stanford question answering data set, and show that it can achieve state-of-the-art performance.