mlsanityData to 14 Oct 2026

Product Question Answering in E-Commerce: A Survey

arXiv 2302.08092 ↗First posted 16 Feb 2023cs.CL cs.IRcited by 10

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Abstract (arXiv)

Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing datasets and evaluation protocols for each setting. We further summarize the most significant challenges that characterize PQA from general QA applications and discuss their corresponding solutions. Finally, we conclude this paper by providing the prospect on several future directions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 38 of its 76 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

38 of 76 references.

References · 38

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  5. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  6. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  7. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  8. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  9. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
  10. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  11. 2019AmazonQA: A Review-Based Question Answering Taskcited by 43
  12. 2021Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledgecited by 40
26 more
  1. 2020Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Productcited by 39
  2. 2020OpinionDigest: A Simple Framework for Opinion Summarizationcited by 37
  3. 2019Towards Knowledge-Based Personalized Product Description Generation in E-commercecited by 32
  4. 2015Addressing Complex and Subjective Product-Related Queries with Customer Reviewscited by 29
  5. 2019Product-Aware Answer Generation in E-Commerce Question-Answeringcited by 27
  6. 2018AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experiencecited by 26
  7. 2020SubjQA: A Dataset for Subjectivity and Review Comprehensioncited by 24
  8. 2021Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Frameworkcited by 21
  9. 2017Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commercecited by 18
  10. 2020Meaningful Answer Generation of E-Commerce Question-Answeringcited by 16
  11. 2021Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modelingcited by 16
  12. 2016Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systemscited by 15
  13. 2019Persona-Aware Tips Generationcited by 12
  14. 2020Opinion-aware Answer Generation for Review-driven Question Answering in E-Commercecited by 12
  15. 2021Answering Product-Questions by Utilizing Questions from Other Contextually Similar Productscited by 12
  16. 2019Review-Driven Answer Generation for Product-Related Questions in E-Commercecited by 10
  17. 2020Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modelingcited by 10
  18. 2021Diverse and Specific Clarification Question Generation with Keywordscited by 10
  19. 2021A Survey on Legal Question Answering Systemscited by 10
  20. 2019Subjective Databasescited by 7
  21. 2020CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answeringcited by 6
  22. 2020Less is More: Rejecting Unreliable Reviews for Product Question Answeringcited by 5
  23. 2020Review-guided Helpful Answer Identification in E-commercecited by 3
  24. 2020Using Large Pretrained Language Models for Answering User Queries from Product Specificationscited by 3
  25. 2021Distantly Supervised Transformers For E-Commerce Product QAcited by 3
  26. 2019AliMe KBQA: Question Answering over Structured Knowledge for E-commerce Customer Servicecited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 8; 2025: 1.

10 papers.

  1. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 46
  2. 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agentscited by 45
  3. 2024IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commercecited by 11
  4. 2024Question Suggestion for Conversational Shopping Assistants Using Product Metadatacited by 8
  5. 2024An Empirical Comparison of Generative Approaches for Product Attribute-Value Identificationcited by 7
  6. 2024BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domaincited by 4
  7. 2023Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledgecited by 0
  8. 2024Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulationcited by 0
  9. 2024Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answeringcited by 0
  10. 2025QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answeringcited by 0