# Meaningful Answer Generation of E-Commerce Question-Answering

arXiv: https://arxiv.org/abs/2011.07307 · HTML: https://mlsanity.com/paper/2011.07307/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 16 of 16)

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- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [xPQA: Cross-Lingual Product Question Answering across 12 Languages](https://mlsanity.com/paper/2305.09249/) (2023)
- [Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning](https://mlsanity.com/paper/2110.12679/) (2021)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [Adaptive loose optimization for robust question answering](https://mlsanity.com/paper/2305.03971/) (2023)
- [Answer Generation for Questions With Multiple Information Sources in E-Commerce](https://mlsanity.com/paper/2111.14003/) (2021)
- [Draft, Command, and Edit: Controllable Text Editing in E-Commerce](https://mlsanity.com/paper/2208.05623/) (2022)
- [Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation](https://mlsanity.com/paper/2401.09785/) (2024)
- [One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning](https://mlsanity.com/paper/2407.09011/) (2024)
- [Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering](https://mlsanity.com/paper/2409.16025/) (2024)
- [MindFlow+: A Self-Evolving Agent for E-Commerce Customer Service](https://mlsanity.com/paper/2507.18884/) (2025)

## Abstract (arXiv)

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we focus on the task of product-aware answer generation, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews. We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.