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AmazonQA: A Review-Based Question Answering Task

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arxiv 1908.04364 v2 pith:ATQ2GCCH submitted 2019-08-12 cs.CL cs.IR

Mansi Gupta , Nitish Kulkarni , Raghuveer Chanda , Anirudha Rayasam , Zachary C Lipton This is my paper · ORCID
classification cs.CLcs.IR
keywords questionanswerreviewsquestionsdatasetgivenproposetask
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Representational Learning of Foundation Models for Recommendation

    cs.IR 2025-06 conditional novelty 6.0 of 10

    A single recommendation model with task-aware Mixture of Low-rank Experts and convergence-based sample scheduling beats baselines on a new 13-task benchmark.

  2. QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new task and model that generates query-focused bullet-point summaries of product reviews with prevalence counts for each key point.

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