Learning from On-Line User Feedback in Neural Question Answering on the Web

Abstract

Question answering promises a means of efficiently searching web-based content repositories such as Wikipedia. However, the systems of this type most prevalent today merely conduct their learning once in an offline training phase while, afterwards, all parameters remain static. Thus, the possibility of improvement over time is precluded. As a consequence of this shortcoming, question answering is not currently taking advantage of the wealth of feedback mechanisms that have become prominent on the web (e. g., buttons for liking, voting, or sharing). This is the first work that introduces a question-answering system for (web-based) content repositories with an on-line mechanism for user feedback. Our efforts have resulted in QApedia - a framework for on-line improvement based on shallow user feedback. In detail, we develop a simple feedback mechanism that allows users to express whether a question was answered satisfactorily or whether a different answer is needed. Even for this simple mechanism, the implementation represents a daunting undertaking due to the complex, multi-staged operations that underlie state-of-the-art systems for neural questions answering. Another challenge with regard to web-based use is that feedback is limited (and possibly even noisy), as the true labels remain unknown. We thus address these challenges through a novel combination of neural question answering and a dynamic process based on distant supervision, asynchronous updates, and an automatic validation of feedback credibility in order to mine high-quality training samples from the web for the purpose of achieving continuous improvement over time. Our QApedia framework is the first question-answering system that continuously refines its capabilities by improving its now dynamic content repository and thus the underlying answer extraction. QApedia not only achieves state-of-the-art results over several benchmarking datasets, but we further show that it successfully manages to learn from shallow user feedback, even when the feedback is noisy or adversarial. Altogether, our extensive experimental evaluation, with more than 2,500 hours of computational experiments, demonstrates that a feedback mechanism as simple as a binary vote (which is widespread on the web) can considerably improve performance when combined with an efficient framework for continuous learning.