AmazonQA: A Review-Based Question Answering Task
This addresses the problem of slow or unavailable answers for Amazon customers by enabling automated QA from reviews, though it is incremental as it builds on existing QA and retrieval methods.
The authors tackled the problem of answering customer questions on Amazon product pages by proposing a review-based QA task, where a system synthesizes answers from product reviews, and introduced a dataset of 923k questions, 3.6M answers, and 14M reviews across 156k products, demonstrating its challenging nature with strong baselines.
Every day, thousands of customers post questions on Amazon product pages. After some time, if they are fortunate, a knowledgeable customer might answer their question. Observing that many questions can be answered based upon the available product reviews, we propose the task of review-based QA. Given a corpus of reviews and a question, the QA system synthesizes an answer. To this end, we introduce a new dataset and propose a method that combines information retrieval techniques for selecting relevant reviews (given a question) and "reading comprehension" models for synthesizing an answer (given a question and review). Our dataset consists of 923k questions, 3.6M answers and 14M reviews across 156k products. Building on the well-known Amazon dataset, we collect additional annotations, marking each question as either answerable or unanswerable based on the available reviews. A deployed system could first classify a question as answerable and then attempt to generate an answer. Notably, unlike many popular QA datasets, here, the questions, passages, and answers are all extracted from real human interactions. We evaluate numerous models for answer generation and propose strong baselines, demonstrating the challenging nature of this new task.