Riker

Abstract

This work studies product question answering (PQA) which aims to answer product-related questions based on customer reviews. Most recent PQA approaches adopt end2end semantic matching methodologies, which map questions and answers to a latent vector space to measure their relevance. Such methods often achieve superior performance but it tends to be difficult to interpret why. On the other hand, simple keyword-based search methods exhibit natural interpretability through matched keywords, but often suffer from the lexical gap problem. In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. Riker mines rich keyword representations of a question with two major components, internal word re-weighting and external word association, which predict the importance of each question word and associate the question with outside relevant keywords respectively, and can be jointly trained under weak supervision with large-scale QA pairs. The keyword representations from Riker can be directly used as input to a keyword-based search module, enabling the whole process to be effective while preserving good interpretability. We conduct extensive experiments using Amazon QA and review datasets from 5 different departments, and our results show that Riker substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations. In addition, we compare keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.