{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/product-aware-answer-generation-in-e-commerce","title":"Product-Aware Answer Generation in E-Commerce Question-Answering","arxiv_id":"1901.07696","date":"2019-01-23","proceeding":null,"authors":["Shen Gao","Zhaochun Ren","Yihong Eric Zhao","Dongyan Zhao","Dawei Yin","Rui Yan"],"abstract":"In e-commerce portals, generating answers for product-related questions has\nbecome a crucial task. In this paper, we propose the task of product-aware\nanswer generation, which tends to generate an accurate and complete answer from\nlarge-scale unlabeled e-commerce reviews and product attributes. Unlike\nexisting question-answering problems, answer generation in e-commerce confronts\nthree main challenges: (1) Reviews are informal and noisy; (2) joint modeling\nof reviews and key-value product attributes is challenging; (3) traditional\nmethods easily generate meaningless answers. To tackle above challenges, we\npropose an adversarial learning based model, named PAAG, which is composed of\nthree components: a question-aware review representation module, a key-value\nmemory network encoding attributes, and a recurrent neural network as a\nsequence generator. Specifically, we employ a convolutional discriminator to\ndistinguish whether our generated answer matches the facts. To extract the\nsalience part of reviews, an attention-based review reader is proposed to\ncapture the most relevant words given the question. Conducted on a large-scale\nreal-world e-commerce dataset, our extensive experiments verify the\neffectiveness of each module in our proposed model. Moreover, our experiments\nshow that our model achieves the state-of-the-art performance in terms of both\nautomatic metrics and human evaluations.","url_abs":"http://arxiv.org/abs/1901.07696v2","url_pdf":"http://arxiv.org/pdf/1901.07696v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"product-aware-answer-generation-in-e-commerce","repo_url":"https://github.com/gsh199449/productqa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"answer-generation","task_name":"Answer Generation"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/product-question-answering-on-jd-product","task":"Question Answering","dataset":"JD Product Question Answer","model":"PAAG","rank_in_archive_order":1,"of":1,"metrics":{"BLEU":"2.0189"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.07696","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}