# Product-Aware Answer Generation in E-Commerce Question-Answering

arXiv: https://arxiv.org/abs/1901.07696 · HTML: https://mlsanity.com/paper/1901.07696/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 27 of 27)

- [Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum](https://mlsanity.com/paper/2308.14034/) (2023)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering](https://mlsanity.com/paper/2311.06503/) (2023)
- [Modern Question Answering Datasets and Benchmarks: A Survey](https://mlsanity.com/paper/2206.15030/) (2022)
- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products](https://mlsanity.com/paper/2105.08956/) (2021)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering](https://mlsanity.com/paper/2403.05217/) (2024)
- [Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://mlsanity.com/paper/2306.03411/) (2023)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain](https://mlsanity.com/paper/2409.20075/) (2024)
- [Distantly Supervised Transformers For E-Commerce Product QA](https://mlsanity.com/paper/2104.02947/) (2021)
- [UMSE: Unified Multi-scenario Summarization Evaluation](https://mlsanity.com/paper/2305.16895/) (2023)
- [Contextually Aware E-Commerce Product Question Answering using RAG](https://mlsanity.com/paper/2508.01990/) (2025)
- [Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization](https://mlsanity.com/paper/2207.07278/) (2022)
- [Robust Domain Adaptation for Machine Reading Comprehension](https://mlsanity.com/paper/2209.11615/) (2022)
- [Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2509.23874/) (2025)
- [Attribute-Aware Controlled Product Generation with LLMs for E-commerce](https://mlsanity.com/paper/2601.04200/) (2025)
- [Abstractive Opinion Tagging](https://mlsanity.com/paper/2101.06880/) (2021)
- [Answer Generation for Questions With Multiple Information Sources in E-Commerce](https://mlsanity.com/paper/2111.14003/) (2021)
- [Extraction of Product Specifications from the Web -- Going Beyond Tables and Lists](https://mlsanity.com/paper/2201.02896/) (2022)
- [SSP: Self-Supervised Post-training for Conversational Search](https://mlsanity.com/paper/2307.00569/) (2023)
- [Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge](https://mlsanity.com/paper/2309.05938/) (2023)
- [Identifying Shopping Intent in Product QA for Proactive Recommendations](https://mlsanity.com/paper/2404.06017/) (2024)
- [Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering](https://mlsanity.com/paper/2409.16025/) (2024)
- [QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering](https://mlsanity.com/paper/2506.04020/) (2025)

## Abstract (arXiv)

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.